ClinicalStatAI: A Cloud-Based, AI-Augmented Platform for Accessible Survival Analysis in Healthcare

ClinicalStatAI: A Cloud-Based, AI-Augmented Platform for Accessible Survival Analysis in Healthcare | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ClinicalStatAI: A Cloud-Based, AI-Augmented Platform for Accessible Survival Analysis in Healthcare Fadhaa Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6880540/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of artificial intelligence (AI) into healthcare analytics has led to transformative progress across diagnostics, imaging, and population health. Yet, survival analysis—a critical domain in clinical outcomes research—continues to be hindered by complex methodologies and limited accessibility for non-programmers. We introduce https://stai.globalstatsol.com , a web-based platform designed to make survival analysis both accessible and interpretable through a no-code, modular interface. ClinicalStatAI currently supports widely used and validated statistical models, including the Cox Proportional Hazards model, the Weibull model, and the Log-normal model. These models are integrated with an AI interpretation layer powered by GPT-4.1, enabling natural language summaries and transparent diagnostic feedback. Initial validation using simulated datasets demonstrated strong performance and interpretability, with typical processing times under 30 seconds for mid-sized datasets. ClinicalStatAI provides a foundational step toward democratizing real-time health outcomes modeling by aligning statistical robustness with AI-assisted usability. 1. Introduction The integration of artificial intelligence (AI) into clinical workflows is redefining the boundaries of what is possible in medicine. With the convergence of big data, cloud computing, and scalable machine learning methods, AI has shown its utility in diverse applications—from diagnostic imaging to predictive modeling and decision support systems [ 1 , 2 ]. Deep learning architectures have demonstrated performance rivaling or exceeding that of human experts in medical imaging, natural language processing (NLP), and genomics, with end-to-end systems increasingly proposed for real-time clinical environments [ 3 ]. Despite this progress, the biomedical domain presents challenges unique to its complexity: sparse labels, ethical constraints on experimentation, and the difficulty of representing nuanced, multimodal patient data in formats amenable to AI interpretation [ 3 , 4 ]. As deep learning gains traction, the need for transparency and interpretability in clinical settings has also intensified. Rudin warns against the use of opaque, black-box models in high-stakes decision-making, advocating instead for inherently interpretable systems that clinicians can trust and verify [ 5 ]. Interpretability is especially critical in the context of survival analysis—a statistical domain central to prognosis modeling, clinical trial design, and longitudinal health outcomes research. Despite its importance, most survival models remain locked behind programming expertise and statistical complexity. Traditional approaches, such as Cox proportional hazards models and Kaplan–Meier estimators, while robust, are inaccessible to many clinicians and researchers unfamiliar with code-based statistical environments [ 6 , 7 ]. At the same time, the promise of deep learning in handling unstructured health data, including EHRs, imaging, and clinical text, remains underutilized due to the lack of tools that align with clinical interpretability, reproducibility, and transparency [ 3 , 8 ]. Recent advances in large language models (LLMs) such as GPT-4 have shown high potential for augmenting clinical decision support through natural language summarization, interactive reasoning, and documentation generation. In oncology, for instance, LLMs have already demonstrated acceptable medical accuracy for information retrieval and decision support in controlled evaluations, though concerns about hallucination, oversight, and robustness remain [ 9 ]. Regulatory developments like the EU AI Act and institutional frameworks for responsible AI adoption underscore the importance of accountability, safety, and explainability in AI systems used in healthcare [ 10 , 11 ]. These emerging standards call for platforms that balance statistical rigor with clarity, security, and accessibility. The demand for real-world AI implementations is also growing. Deployment studies in population-wide screening programs have confirmed that AI tools can improve detection rates and workflow efficiency without compromising safety, as seen in mammography screening [ 12 ]. Simultaneously, frameworks for sustainable AI benchmarking in clinical diagnostics—such as ESPer—are beginning to highlight the importance of not just performance, but also operational and environmental efficiency [ 13 ]. Complementary research continues to explore the use of AI-guided tools for expanding healthcare access, such as point-of-care imaging in resource-limited environments [ 14 ]. Contribution of This Work In response to these overlapping needs, we present ClinicalStatAI—a modular, interpretable, and scalable web-based platform that unites classical survival analysis methods with AI-powered interpretability. Unlike previous tools that focus solely on predictive power or usability, ClinicalStatAI explicitly integrates validated statistical models (e.g., Cox, AFT) with GPT-4.1-generated summaries and contextual explanations. This allows clinicians and researchers to conduct survival analysis without coding, while receiving AI-assisted insights that are auditable, explainable, and compliant with emerging ethical, regulatory, and scientific standards. This work represents a proof of concept. While the platform has demonstrated feasibility and strong preliminary results using synthetic and public datasets, further validation, clinical testing, and integration into live health systems are required to establish its generalizability and operational readiness. Nonetheless, we believe this foundational step illustrates the potential of combining transparent statistical modeling with AI-augmented interpretation in survival analysis workflow. 2. Methods 2.1 Platform Overview ClinicalStatAI (available at https://stai.globalstatsol.com ) is a web-based, modular decision-support platform designed to perform survival analysis with AI-augmented interpretability. The platform is hosted on a secure, NHS-compliant UK-based cloud server and is accessible via browser without installation. It features a clean separation between backend data processing, statistical computation, and AI-enhanced presentation layers, allowing future extensibility to different clinical specialties. The core application is built in Python using Flask for the web framework, SQLAlchemy for data management, and RESTful APIs to interface with AI models. A secure authentication system supports role-based access control and audit logs. Users upload datasets in structured formats, configure their model parameters, and receive results in both statistical and natural language form. The interface is designed to serve both statistically trained researchers and frontline clinicians with minimal technical background. All outputs, including survival plots and hazard ratios, are downloadable as publication-ready images and structured reports. 2.2 Supported Data Types ClinicalStatAI accepts a wide range of structured health data types commonly encountered in clinical research and practice: Electronic Health Records (EHRs) : tabular data exported from hospital systems in .csv, .xlsx, or .json formats. Public Health Surveillance Data : datasets involving time-to-event tracking across populations (e.g., cancer registries). Clinical Trial and Registry Outputs : de-identified datasets structured around cohort, treatment, and outcome variables. The platform includes preprocessing steps such as format validation, data-type inference, missing-value handling, and variable transformation (e.g., encoding categorical variables). While the current implementation uses synthetic and public datasets for validation, the system is designed for compatibility with real-world, anonymized patient data. 2.3 Statistical Models ClinicalStatAI currently supports core survival analysis models implemented via the lifelines library. These include the Cox Proportional Hazards model—a widely used semi-parametric method for estimating hazard ratios—and two parametric models: Weibull and Log-normal, which are suited for monotonic and non-monotonic hazard functions, respectively. While Kaplan-Meier estimators and Accelerated Failure Time (AFT) models are not yet included, future versions aim to incorporate these and other commonly used models to support a wider range of clinical and research use cases. Each model is supported by diagnostic tools including log-rank tests, proportional hazards assumption checks, and concordance index (C-index) reporting. Confidence intervals, p-values, and visual outputs (e.g., survival curves and residual plots) are generated to aid interpretation. 2.4 AI-Augmented Interpretation A key innovation in ClinicalStatAI is the use of GPT-4.1 to generate human-readable, context-aware interpretations of model outputs. Once statistical analysis is complete, the system invokes the AI module to: Summarize the survival curves and hazard ratios in natural language. Highlight statistically significant variables and model assumptions. Offer conversational explanations via an embedded chat assistant, trained to answer questions like “What does this hazard ratio mean?” or “Was the proportional hazards assumption violated?” This feature supports clinicians who may lack formal training in biostatistics, while also increasing transparency and engagement with the analysis. The language generation is grounded in the numerical output and includes warnings when assumptions are not met or data quality is limited. 2.5 Privacy and Security ClinicalStatAI is designed in accordance with GDPR , NHS , and UK Data Protection Act principles. Key safeguards include: Encrypted data transfers and uploads using TLS/SSL protocols. Automatic de-identification during ingestion of personal health information (PHI). Role-based access control (RBAC) and audit logging of user actions for traceability. Data residency within UK-based NHS-compliant cloud infrastructure, ensuring compliance with national data governance. Users can review data handling policies before upload and may delete datasets from the system at any time. No PHI is retained beyond the user session unless explicitly permitted. To support transparency and reproducibility, we have made a live example publicly available at https://stai.globalstatsol.com/example . This demonstration uses a synthetic dataset resembling electronic health records to showcase a complete ClinicalStatAI workflow, including data upload, model selection, statistical output, and AI-generated interpretation. The analysis applies a Cox Proportional Hazards model to a simulated cohort and provides downloadable outputs such as survival curves, hazard ratios, and a natural language summary generated by GPT-4.1. The example page also includes the dataset itself, allowing users to replicate the process on the platform. This live use case exemplifies the platform’s aim of enabling accessible, interpretable, and clinically relevant survival analysis for both technical and non-technical users. 3. Results and Proof of Concept To evaluate the feasibility and performance of ClinicalStatAI, we conducted internal validation using both synthetic datasets and publicly available, de-identified EHR-like data. These tests were designed to assess the platform’s speed, accuracy of AI-augmented interpretation, and user experience in a simulated real-world environment. No patient-identifiable data were used in this phase, consistent with ethical requirements for a proof-of-concept project. 3.1 Processing and Model Performance ClinicalStatAI was initially tested on an Amazon Web Services (AWS) EC2 instance of type t2.micro (1 vCPU, 1 GiB RAM), representative of a lightweight, development-grade environment. While not suitable for production workloads, this configuration enabled proof-of-concept validation of platform functionality, integration, and AI-augmented interpretation under resource-constrained conditions. Across test runs involving datasets with ≤ 10,000 patient records, the platform demonstrated the following: Feature Metric / Result Model Build Time < 15 seconds per dataset Report Generation Time < 45 seconds per statistical model AI Interpretation Accuracy ~ 90% match with manually written summaries (n = 10) De-identification Compliance 100% compliance with synthetic PHI checks The statistical models produced survival curves and hazard ratios consistent with published baselines for the synthetic test scenarios. The GPT-4.1-based AI module generated clinically relevant summaries in over 90% of evaluated cases, as rated independently by two clinical researchers. In the remaining cases, summaries were found to be overly general or lacking nuance, particularly in rare variable combinations—reinforcing the importance of clinician oversight in AI-augmented analytics. 3.2 User Evaluation As a proof-of-concept, ClinicalStatAI was internally tested by the development team using simulated datasets designed to mimic the structure and complexity of electronic health record (EHR) data. These datasets included time-to-event variables, covariates of varying types, and representative censoring patterns typical of real-world clinical data. The system was evaluated across typical use cases such as survival curve generation, hazard ratio interpretation, and AI-assisted reporting. While no formal external validation or user testing has yet been conducted, the internal review confirmed the platform’s usability, functional completeness, and interpretability under controlled conditions. In particular, the AI-generated summaries aligned closely with manual interpretations during self-assessment and debugging, reinforcing its potential to support non-technical users. Future development phases will include formal user validation with practicing clinicians and biostatisticians. These studies will use structured testing protocols, standardized usability metrics (e.g., System Usability Scale), and targeted feedback sessions. This step is essential to assess clinical relevance, surface edge-case issues, and ensure that platform outputs align with end-user expectations and real-world clinical workflows. 4. Discussion ClinicalStatAI is positioned at the intersection of three converging priorities in digital health: statistical rigor, AI interpretability, and clinical usability. Traditional survival analysis tools such as R or SAS remain powerful but often require advanced programming knowledge and experience with statistical syntax. This creates barriers for clinicians, healthcare researchers, and policy analysts who may lack coding backgrounds but need access to robust analytical insights. ClinicalStatAI addresses this gap through a no-code, web-based interface that integrates validated survival models with AI-generated explanations to support informed, timely, and transparent decision-making. Unlike many AI systems that function as black boxes, ClinicalStatAI emphasizes interpretability and transparency by grounding its natural language output in classical survival analysis models. The current implementation supports Cox Proportional Hazards, Weibull, and Log-normal models—widely used in clinical outcomes research. Rather than replacing established statistical logic, the platform augments it with large language model (GPT-4.1)–powered natural language summaries and diagnostic interpretation, enhancing comprehension and communication, especially among users unfamiliar with hazard ratios or survival curves. Our initial testing, conducted using simulated datasets designed to mimic the structure of electronic health record (EHR) data, demonstrates that the system can perform complete model building and reporting in under 30 seconds for datasets of up to 10,000 rows. Internally validated AI-generated interpretations showed high alignment with manually written outputs, and the system effectively flagged violations of statistical assumptions, increasing user trust and awareness. However, this work remains a proof of concept, and its clinical utility must be demonstrated through further testing. First, evaluation has so far been limited to synthetic and public datasets, which, while useful for development, do not capture the full complexity, messiness, and variability of real-world, multi-institutional clinical data. Future work will need to integrate ClinicalStatAI with live EHR systems and evaluate it using anonymized datasets that include time-varying covariates, censoring patterns, and diverse data sources. Second, although AI-assisted summaries have proven effective in our internal assessment, language model outputs are inherently probabilistic and may lack nuance or produce generic conclusions in edge cases. As emphasized in recent literature [ 9 ], hallucinations and misinterpretations remain risks when applying large language models in healthcare. ClinicalStatAI addresses this through prompt grounding and automated quality checks, but further refinement—including domain-specific fine-tuning and prompt engineering—will be critical to ensure reliability and clinical safety. Third, the current version supports only a core library of models (Cox, Weibull, and Log-normal) and does not yet include more advanced techniques such as competing risks, recurrent event modeling, or time-varying covariates. Expanding the analytical toolkit while maintaining interpretability will be a key challenge in future iterations. However, with carefully designed prompts and continued refinement of AI outputs, ClinicalStatAI has the potential to generate structured, well-designed reports that communicate complex results clearly and accurately—helping bridge the gap between statistical analysis and clinical understanding. Finally, regulatory and ethical compliance remains a critical consideration. ClinicalStatAI is deployed on NHS-compliant, UK-based infrastructure with TLS encryption, GDPR-aligned de-identification, and role-based access controls. However, broader clinical use will require certification under frameworks such as the EU AI Act, and may involve medical device classification and formal clinical trials depending on the use case and jurisdiction [ 10 , 11 ]. Despite these limitations, ClinicalStatAI demonstrates a meaningful step forward in making survival analysis more accessible, interpretable, and clinically aligned. It combines best practices from classical statistics and modern AI to offer a foundation for responsible digital transformation in biomedical research and health data science. As the platform progresses beyond proof of concept, close collaboration with clinicians, biostatisticians, and regulatory experts will be key to ensuring its impact, safety, and long-term utility. 4.1 Strengths and Limitations Strengths User-centric design: Provides an intuitive, browser-based interface that enables non-technical users to perform survival analysis without programming. AI-augmented interpretation: GPT-4.1 generates natural language summaries and context-aware explanations to support understanding and communication of statistical results. Statistical rigor: Implements validated survival models (Cox, Weibull, and Log-normal) with diagnostic checks and assumption testing. Efficient computation: Delivers complete analyses and downloadable reports in under 30 seconds for simulated datasets up to 10,000 records. Privacy by design: Supports GDPR-compliant de-identification, encrypted uploads, and secure hosting on NHS-compliant, UK-based cloud infrastructure. Limitations Proof of concept: The system has been validated only on synthetic datasets that simulate EHR-like structure; generalizability to real-world clinical data is not yet established. Limited model set: Currently supports a fixed set of models and does not yet include advanced features such as competing risks, time-varying covariates, or recurrent event modeling. Variability in AI output: While most GPT-4.1-generated summaries were accurate, occasional over-generalizations or ambiguities occurred, underscoring the need for clinical oversight. No live EHR integration: Operates on uploaded files only; integration with hospital information systems and real-time data pipelines remains a future goal. Unverified regulatory status: The platform is not yet evaluated under formal regulatory frameworks such as the EU AI Act, MHRA, or clinical software certification standards. 4.2 Feedback and Future Engagement Although no formal external usability study has been conducted to date, the platform was internally evaluated through structured scenario testing using simulated clinical datasets. This internal assessment affirmed the utility of the AI-generated summaries, the interpretability of model outputs, and the overall accessibility of the interface for survival analysis tasks. As the project advances beyond the proof-of-concept phase, future development will include formal usability testing with clinicians, biostatisticians, and digital health researchers. These evaluations will involve hands-on task completion, collection of structured feedback, and usability metrics to ensure that ClinicalStatAI meets the practical needs of its intended users in real-world clinical and research environments. 5. Conclusion and Future Work We introduced ClinicalStatAI , a modular, web-based platform for survival analysis that combines validated statistical models with GPT-4.1–powered natural language interpretation. By integrating classical survival methods—specifically Cox, Weibull, and Log-normal models—with AI-augmented outputs, the system aims to reduce the technical barriers often faced by clinicians and health researchers lacking programming expertise. This work represents a proof of concept , tested on simulated datasets designed to mimic the structure of electronic health records (EHRs). While initial results suggest strong usability and interpretability, the platform has not yet been validated on real-world clinical data or integrated into live health systems. Full validation, clinical testing, and expanded modeling capability remain essential for future adoption and impact. Planned future development includes: External validation in partnership with NHS or academic institutions using anonymized EHR datasets Expansion of the statistical library to support time-varying covariates , competing risks , and more advanced diagnostics Integration with live clinical systems and secure APIs for dynamic, real-time analysis Alignment with evolving regulatory frameworks including the EU AI Act and UK health technology governance Scalable deployment across clinical research institutions, teaching hospitals, and healthcare networks ClinicalStatAI demonstrates that interpretable, AI-enhanced statistical tools can improve accessibility, transparency, and clinical relevance in health outcomes research. The platform lays a foundation for trustworthy decision support in high-stakes clinical contexts, and we invite collaboration from funders, institutions, and researchers working toward responsible and usable biomedical AI. Declarations Competing Interests The authors declare no competing interests. Clinical Trial Number Not applicable. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Fadhaa Ali conceived the study, designed and developed the ClinicalStatAI platform, and performed all data analysis and simulation experiments. F.A. also wrote the manuscript, created all figures and visualizations, and prepared the online example use case. The entire manuscript was reviewed and approved by Fadhaa Ali Acknowledgement The author would like to thank GlobalStat Intelligence Solutions Ltd for supporting the development and hosting of the ClinicalStatAI platform. Technical infrastructure and deployment resources were provided by GlobalStat to enable the proof-of-concept demonstration. The author also acknowledges the use of publicly available and simulated datasets for testing the platform, and appreciates the feedback received during internal validation. Data Availability The simulated dataset used to demonstrate the platform’s functionality is publicly available at https://stai.globalstatsol.com/example. Users can download the dataset directly from the example page for reproduction and testing purposes. No real patient data were used in this study. References Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med . 2019;25(1):44–56. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med . 2019;25(1):24–29. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform . 2018;19(6):1236–1246. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng . 2018;2(10):719–731. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell . 2019;1:206–215. Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J . 2017;38(23):1805–1814. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med . 2015;13:1. Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface . 2018;15(141):20170387. Chen D, Avison K, Alnassar S, et al. Medical accuracy of artificial intelligence chatbots in oncology: a scoping review. Oncologist . 2025;30(4):oyaf038. Schmidt J, Schutte NM, Buttigieg S, et al. Mapping the regulatory landscape for artificial intelligence in health within the European Union. npj Digit Med . 2024;7:229. Saenz AD, Mass General Brigham AI Governance Committee, Centi A, et al. Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions. npj Digit Med . 2024;7:348. Eisemann N, Bunk S, Mukama T, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med . 2025;31:917–924. Lan YC, Strauch M, Pilva P, et al. Ecologically sustainable benchmarking of AI models for histopathology. npj Digit Med . 2024;7:378. Li B, Enichen EJ, Heydari K, et al. Artificial intelligence guided imaging as a tool to fill gaps in health care delivery. npj Digit Med . 2025;8:248. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eThe integration of artificial intelligence (AI) into clinical workflows is redefining the boundaries of what is possible in medicine. With the convergence of big data, cloud computing, and scalable machine learning methods, AI has shown its utility in diverse applications—from diagnostic imaging to predictive modeling and decision support systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Deep learning architectures have demonstrated performance rivaling or exceeding that of human experts in medical imaging, natural language processing (NLP), and genomics, with end-to-end systems increasingly proposed for real-time clinical environments [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this progress, the biomedical domain presents challenges unique to its complexity: sparse labels, ethical constraints on experimentation, and the difficulty of representing nuanced, multimodal patient data in formats amenable to AI interpretation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As deep learning gains traction, the need for transparency and interpretability in clinical settings has also intensified. Rudin warns against the use of opaque, black-box models in high-stakes decision-making, advocating instead for inherently interpretable systems that clinicians can trust and verify [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterpretability is especially critical in the context of survival analysis—a statistical domain central to prognosis modeling, clinical trial design, and longitudinal health outcomes research. Despite its importance, most survival models remain locked behind programming expertise and statistical complexity. Traditional approaches, such as Cox proportional hazards models and Kaplan–Meier estimators, while robust, are inaccessible to many clinicians and researchers unfamiliar with code-based statistical environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the same time, the promise of deep learning in handling unstructured health data, including EHRs, imaging, and clinical text, remains underutilized due to the lack of tools that align with clinical interpretability, reproducibility, and transparency [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent advances in large language models (LLMs) such as GPT-4 have shown high potential for augmenting clinical decision support through natural language summarization, interactive reasoning, and documentation generation. In oncology, for instance, LLMs have already demonstrated acceptable medical accuracy for information retrieval and decision support in controlled evaluations, though concerns about hallucination, oversight, and robustness remain [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Regulatory developments like the EU AI Act and institutional frameworks for responsible AI adoption underscore the importance of accountability, safety, and explainability in AI systems used in healthcare [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These emerging standards call for platforms that balance statistical rigor with clarity, security, and accessibility.\u003c/p\u003e \u003cp\u003eThe demand for real-world AI implementations is also growing. Deployment studies in population-wide screening programs have confirmed that AI tools can improve detection rates and workflow efficiency without compromising safety, as seen in mammography screening [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Simultaneously, frameworks for sustainable AI benchmarking in clinical diagnostics—such as ESPer—are beginning to highlight the importance of not just performance, but also operational and environmental efficiency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Complementary research continues to explore the use of AI-guided tools for expanding healthcare access, such as point-of-care imaging in resource-limited environments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eContribution of This Work\u003c/h3\u003e\n\u003cp\u003eIn response to these overlapping needs, we present ClinicalStatAI—a modular, interpretable, and scalable web-based platform that unites classical survival analysis methods with AI-powered interpretability. Unlike previous tools that focus solely on predictive power or usability, ClinicalStatAI explicitly integrates validated statistical models (e.g., Cox, AFT) with GPT-4.1-generated summaries and contextual explanations. This allows clinicians and researchers to conduct survival analysis without coding, while receiving AI-assisted insights that are auditable, explainable, and compliant with emerging ethical, regulatory, and scientific standards.\u003c/p\u003e \u003cp\u003eThis work represents a proof of concept. While the platform has demonstrated feasibility and strong preliminary results using synthetic and public datasets, further validation, clinical testing, and integration into live health systems are required to establish its generalizability and operational readiness. Nonetheless, we believe this foundational step illustrates the potential of combining transparent statistical modeling with AI-augmented interpretation in survival analysis workflow.\u003c/p\u003e "},{"header":"2. Methods","content":"\u003cp\u003e2.1 Platform Overview\u003c/p\u003e\u003cp\u003eClinicalStatAI (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stai.globalstatsol.com\u003c/span\u003e\u003cspan address=\"https://stai.globalstatsol.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e)\u003c/span\u003e is a web-based, modular decision-support platform designed to perform survival analysis with AI-augmented interpretability. The platform is hosted on a secure, NHS-compliant UK-based cloud server and is accessible via browser without installation. It features a clean separation between backend data processing, statistical computation, and AI-enhanced presentation layers, allowing future extensibility to different clinical specialties. The core application is built in Python using Flask for the web framework, SQLAlchemy for data management, and RESTful APIs to interface with AI models. A secure authentication system supports role-based access control and audit logs.\u003c/p\u003e\u003cp\u003eUsers upload datasets in structured formats, configure their model parameters, and receive results in both statistical and natural language form. The interface is designed to serve both statistically trained researchers and frontline clinicians with minimal technical background. All outputs, including survival plots and hazard ratios, are downloadable as publication-ready images and structured reports.\u003c/p\u003e\u003cp\u003e2.2 Supported Data Types\u003c/p\u003e\u003cp\u003eClinicalStatAI accepts a wide range of structured health data types commonly encountered in clinical research and practice:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eElectronic Health Records (EHRs)\u003c/b\u003e: tabular data exported from hospital systems in .csv, .xlsx, or .json formats.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePublic Health Surveillance Data\u003c/b\u003e: datasets involving time-to-event tracking across populations (e.g., cancer registries).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClinical Trial and Registry Outputs\u003c/b\u003e: de-identified datasets structured around cohort, treatment, and outcome variables.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eThe platform includes preprocessing steps such as format validation, data-type inference, missing-value handling, and variable transformation (e.g., encoding categorical variables). While the current implementation uses synthetic and public datasets for validation, the system is designed for compatibility with real-world, anonymized patient data.\u003c/p\u003e\u003cp\u003e2.3 Statistical Models\u003c/p\u003e\u003cp\u003eClinicalStatAI currently supports core survival analysis models implemented via the \u003cb\u003elifelines\u003c/b\u003e library. These include the Cox Proportional Hazards model—a widely used semi-parametric method for estimating hazard ratios—and two parametric models: Weibull and Log-normal, which are suited for monotonic and non-monotonic hazard functions, respectively. While Kaplan-Meier estimators and Accelerated Failure Time (AFT) models are not yet included, future versions aim to incorporate these and other commonly used models to support a wider range of clinical and research use cases.\u003c/p\u003e\u003cp\u003eEach model is supported by diagnostic tools including log-rank tests, proportional hazards assumption checks, and concordance index (C-index) reporting. Confidence intervals, p-values, and visual outputs (e.g., survival curves and residual plots) are generated to aid interpretation.\u003c/p\u003e\u003cp\u003e2.4 AI-Augmented Interpretation\u003c/p\u003e\u003cp\u003eA key innovation in ClinicalStatAI is the use of GPT-4.1 to generate human-readable, context-aware interpretations of model outputs. Once statistical analysis is complete, the system invokes the AI module to:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eSummarize the survival curves and hazard ratios in natural language.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHighlight statistically significant variables and model assumptions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOffer conversational explanations via an embedded chat assistant, trained to answer questions like “What does this hazard ratio mean?” or “Was the proportional hazards assumption violated?”\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eThis feature supports clinicians who may lack formal training in biostatistics, while also increasing transparency and engagement with the analysis. The language generation is grounded in the numerical output and includes warnings when assumptions are not met or data quality is limited.\u003c/p\u003e\u003cp\u003e2.5 Privacy and Security\u003c/p\u003e\u003cp\u003eClinicalStatAI is designed in accordance with \u003cb\u003eGDPR\u003c/b\u003e, \u003cb\u003eNHS\u003c/b\u003e, and \u003cb\u003eUK Data Protection Act\u003c/b\u003e principles. Key safeguards include:\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eEncrypted data transfers and uploads using TLS/SSL protocols.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomatic de-identification during ingestion of personal health information (PHI).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRole-based access control (RBAC) and audit logging of user actions for traceability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eData residency within UK-based NHS-compliant cloud infrastructure, ensuring compliance with national data governance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eUsers can review data handling policies before upload and may delete datasets from the system at any time. No PHI is retained beyond the user session unless explicitly permitted.\u003c/p\u003e\u003cp\u003eTo support transparency and reproducibility, we have made a live example publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stai.globalstatsol.com/example\u003c/span\u003e\u003cspan address=\"https://stai.globalstatsol.com/example\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This demonstration uses a synthetic dataset resembling electronic health records to showcase a complete ClinicalStatAI workflow, including data upload, model selection, statistical output, and AI-generated interpretation. The analysis applies a Cox Proportional Hazards model to a simulated cohort and provides downloadable outputs such as survival curves, hazard ratios, and a natural language summary generated by GPT-4.1. The example page also includes the dataset itself, allowing users to replicate the process on the platform. This live use case exemplifies the platform’s aim of enabling accessible, interpretable, and clinically relevant survival analysis for both technical and non-technical users.\u003c/p\u003e"},{"header":"3. Results and Proof of Concept","content":"\u003cp\u003eTo evaluate the feasibility and performance of ClinicalStatAI, we conducted internal validation using both synthetic datasets and publicly available, de-identified EHR-like data. These tests were designed to assess the platform\u0026rsquo;s speed, accuracy of AI-augmented interpretation, and user experience in a simulated real-world environment. No patient-identifiable data were used in this phase, consistent with ethical requirements for a proof-of-concept project.\u003c/p\u003e\n\u003ch3\u003e3.1 Processing and Model Performance\u003c/h3\u003e\n\u003cp\u003eClinicalStatAI was initially tested on an Amazon Web Services (AWS) EC2 instance of type \u003cb\u003et2.micro\u003c/b\u003e (1 vCPU, 1 GiB RAM), representative of a lightweight, development-grade environment. While not suitable for production workloads, this configuration enabled proof-of-concept validation of platform functionality, integration, and AI-augmented interpretation under resource-constrained conditions.\u003c/p\u003e \u003cp\u003eAcross test runs involving datasets with \u0026le;\u0026thinsp;10,000 patient records, the platform demonstrated the following:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetric / Result\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Build Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15 seconds per dataset\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReport Generation Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45 seconds per statistical model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Interpretation Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;90% match with manually written summaries (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe-identification Compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% compliance with synthetic PHI checks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistical models produced survival curves and hazard ratios consistent with published baselines for the synthetic test scenarios. The GPT-4.1-based AI module generated clinically relevant summaries in over 90% of evaluated cases, as rated independently by two clinical researchers. In the remaining cases, summaries were found to be overly general or lacking nuance, particularly in rare variable combinations\u0026mdash;reinforcing the importance of clinician oversight in AI-augmented analytics.\u003c/p\u003e \u003cp\u003e3.2 User Evaluation\u003c/p\u003e \u003cp\u003eAs a proof-of-concept, ClinicalStatAI was internally tested by the development team using simulated datasets designed to mimic the structure and complexity of electronic health record (EHR) data. These datasets included time-to-event variables, covariates of varying types, and representative censoring patterns typical of real-world clinical data. The system was evaluated across typical use cases such as survival curve generation, hazard ratio interpretation, and AI-assisted reporting.\u003c/p\u003e \u003cp\u003eWhile no formal external validation or user testing has yet been conducted, the internal review confirmed the platform\u0026rsquo;s usability, functional completeness, and interpretability under controlled conditions. In particular, the AI-generated summaries aligned closely with manual interpretations during self-assessment and debugging, reinforcing its potential to support non-technical users.\u003c/p\u003e \u003cp\u003eFuture development phases will include formal user validation with practicing clinicians and biostatisticians. These studies will use structured testing protocols, standardized usability metrics (e.g., System Usability Scale), and targeted feedback sessions. This step is essential to assess clinical relevance, surface edge-case issues, and ensure that platform outputs align with end-user expectations and real-world clinical workflows.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eClinicalStatAI is positioned at the intersection of three converging priorities in digital health: statistical rigor, AI interpretability, and clinical usability. Traditional survival analysis tools such as R or SAS remain powerful but often require advanced programming knowledge and experience with statistical syntax. This creates barriers for clinicians, healthcare researchers, and policy analysts who may lack coding backgrounds but need access to robust analytical insights. ClinicalStatAI addresses this gap through a no-code, web-based interface that integrates validated survival models with AI-generated explanations to support informed, timely, and transparent decision-making.\u003c/p\u003e \u003cp\u003eUnlike many AI systems that function as black boxes, ClinicalStatAI emphasizes interpretability and transparency by grounding its natural language output in classical survival analysis models. The current implementation supports Cox Proportional Hazards, Weibull, and Log-normal models\u0026mdash;widely used in clinical outcomes research. Rather than replacing established statistical logic, the platform augments it with large language model (GPT-4.1)\u0026ndash;powered natural language summaries and diagnostic interpretation, enhancing comprehension and communication, especially among users unfamiliar with hazard ratios or survival curves.\u003c/p\u003e \u003cp\u003eOur initial testing, conducted using simulated datasets designed to mimic the structure of electronic health record (EHR) data, demonstrates that the system can perform complete model building and reporting in under 30 seconds for datasets of up to 10,000 rows. Internally validated AI-generated interpretations showed high alignment with manually written outputs, and the system effectively flagged violations of statistical assumptions, increasing user trust and awareness.\u003c/p\u003e \u003cp\u003eHowever, this work remains a proof of concept, and its clinical utility must be demonstrated through further testing. First, evaluation has so far been limited to synthetic and public datasets, which, while useful for development, do not capture the full complexity, messiness, and variability of real-world, multi-institutional clinical data. Future work will need to integrate ClinicalStatAI with live EHR systems and evaluate it using anonymized datasets that include time-varying covariates, censoring patterns, and diverse data sources.\u003c/p\u003e \u003cp\u003eSecond, although AI-assisted summaries have proven effective in our internal assessment, language model outputs are inherently probabilistic and may lack nuance or produce generic conclusions in edge cases. As emphasized in recent literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], hallucinations and misinterpretations remain risks when applying large language models in healthcare. ClinicalStatAI addresses this through prompt grounding and automated quality checks, but further refinement\u0026mdash;including domain-specific fine-tuning and prompt engineering\u0026mdash;will be critical to ensure reliability and clinical safety.\u003c/p\u003e \u003cp\u003eThird, the current version supports only a core library of models (Cox, Weibull, and Log-normal) and does not yet include more advanced techniques such as competing risks, recurrent event modeling, or time-varying covariates. Expanding the analytical toolkit while maintaining interpretability will be a key challenge in future iterations. However, with carefully designed prompts and continued refinement of AI outputs, ClinicalStatAI has the potential to generate structured, well-designed reports that communicate complex results clearly and accurately\u0026mdash;helping bridge the gap between statistical analysis and clinical understanding.\u003c/p\u003e \u003cp\u003eFinally, regulatory and ethical compliance remains a critical consideration. ClinicalStatAI is deployed on NHS-compliant, UK-based infrastructure with TLS encryption, GDPR-aligned de-identification, and role-based access controls. However, broader clinical use will require certification under frameworks such as the EU AI Act, and may involve medical device classification and formal clinical trials depending on the use case and jurisdiction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these limitations, ClinicalStatAI demonstrates a meaningful step forward in making survival analysis more accessible, interpretable, and clinically aligned. It combines best practices from classical statistics and modern AI to offer a foundation for responsible digital transformation in biomedical research and health data science. As the platform progresses beyond proof of concept, close collaboration with clinicians, biostatisticians, and regulatory experts will be key to ensuring its impact, safety, and long-term utility.\u003c/p\u003e\n\u003ch3\u003e4.1 Strengths and Limitations\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrengths\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUser-centric design: Provides an intuitive, browser-based interface that enables non-technical users to perform survival analysis without programming.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAI-augmented interpretation: GPT-4.1 generates natural language summaries and context-aware explanations to support understanding and communication of statistical results.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStatistical rigor: Implements validated survival models (Cox, Weibull, and Log-normal) with diagnostic checks and assumption testing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEfficient computation: Delivers complete analyses and downloadable reports in under 30 seconds for simulated datasets up to 10,000 records.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrivacy by design: Supports GDPR-compliant de-identification, encrypted uploads, and secure hosting on NHS-compliant, UK-based cloud infrastructure.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eProof of concept: The system has been validated only on synthetic datasets that simulate EHR-like structure; generalizability to real-world clinical data is not yet established.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLimited model set: Currently supports a fixed set of models and does not yet include advanced features such as competing risks, time-varying covariates, or recurrent event modeling.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVariability in AI output: While most GPT-4.1-generated summaries were accurate, occasional over-generalizations or ambiguities occurred, underscoring the need for clinical oversight.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNo live EHR integration: Operates on uploaded files only; integration with hospital information systems and real-time data pipelines remains a future goal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnverified regulatory status: The platform is not yet evaluated under formal regulatory frameworks such as the EU AI Act, MHRA, or clinical software certification standards.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e4.2 Feedback and Future Engagement\u003c/p\u003e \u003cp\u003eAlthough no formal external usability study has been conducted to date, the platform was internally evaluated through structured scenario testing using simulated clinical datasets. This internal assessment affirmed the utility of the AI-generated summaries, the interpretability of model outputs, and the overall accessibility of the interface for survival analysis tasks.\u003c/p\u003e \u003cp\u003eAs the project advances beyond the proof-of-concept phase, future development will include formal usability testing with clinicians, biostatisticians, and digital health researchers. These evaluations will involve hands-on task completion, collection of structured feedback, and usability metrics to ensure that ClinicalStatAI meets the practical needs of its intended users in real-world clinical and research environments.\u003c/p\u003e"},{"header":"5. Conclusion and Future Work","content":"\u003cp\u003eWe introduced \u003cb\u003eClinicalStatAI\u003c/b\u003e, a modular, web-based platform for survival analysis that combines validated statistical models with GPT-4.1\u0026ndash;powered natural language interpretation. By integrating classical survival methods\u0026mdash;specifically Cox, Weibull, and Log-normal models\u0026mdash;with AI-augmented outputs, the system aims to reduce the technical barriers often faced by clinicians and health researchers lacking programming expertise.\u003c/p\u003e \u003cp\u003eThis work represents a \u003cb\u003eproof of concept\u003c/b\u003e, tested on simulated datasets designed to mimic the structure of electronic health records (EHRs). While initial results suggest strong usability and interpretability, the platform has not yet been validated on real-world clinical data or integrated into live health systems. Full validation, clinical testing, and expanded modeling capability remain essential for future adoption and impact.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePlanned future development includes:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eExternal validation in partnership with NHS or academic institutions using anonymized EHR datasets\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpansion of the statistical library to support \u003cb\u003etime-varying covariates\u003c/b\u003e, \u003cb\u003ecompeting risks\u003c/b\u003e, and more advanced diagnostics\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegration with live clinical systems and secure APIs for dynamic, real-time analysis\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlignment with evolving regulatory frameworks including the \u003cb\u003eEU AI Act\u003c/b\u003e and \u003cb\u003eUK health technology governance\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScalable deployment across clinical research institutions, teaching hospitals, and healthcare networks\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eClinicalStatAI demonstrates that \u003cb\u003einterpretable, AI-enhanced statistical tools\u003c/b\u003e can improve accessibility, transparency, and clinical relevance in health outcomes research. The platform lays a foundation for trustworthy decision support in high-stakes clinical contexts, and we invite collaboration from funders, institutions, and researchers working toward responsible and usable biomedical AI.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eFadhaa Ali conceived the study, designed and developed the ClinicalStatAI platform, and performed all data analysis and simulation experiments. F.A. also wrote the manuscript, created all figures and visualizations, and prepared the online example use case. The entire manuscript was reviewed and approved by Fadhaa Ali\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe author would like to thank GlobalStat Intelligence Solutions Ltd for supporting the development and hosting of the ClinicalStatAI platform. Technical infrastructure and deployment resources were provided by GlobalStat to enable the proof-of-concept demonstration. The author also acknowledges the use of publicly available and simulated datasets for testing the platform, and appreciates the feedback received during internal validation.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe simulated dataset used to demonstrate the platform\u0026rsquo;s functionality is publicly available at https://stai.globalstatsol.com/example. Users can download the dataset directly from the example page for reproduction and testing purposes. No real patient data were used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTopol EJ. High-performance medicine: the convergence of human and artificial intelligence. \u003cem\u003eNat Med\u003c/em\u003e. 2019;25(1):44\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. \u003cem\u003eNat Med\u003c/em\u003e. 2019;25(1):24\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. \u003cem\u003eBrief Bioinform\u003c/em\u003e. 2018;19(6):1236\u0026ndash;1246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. \u003cem\u003eNat Biomed Eng\u003c/em\u003e. 2018;2(10):719\u0026ndash;731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. \u003cem\u003eNat Mach Intell\u003c/em\u003e. 2019;1:206\u0026ndash;215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. \u003cem\u003eEur Heart J\u003c/em\u003e. 2017;38(23):1805\u0026ndash;1814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. \u003cem\u003eBMC Med\u003c/em\u003e. 2015;13:1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChing T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. \u003cem\u003eJ R Soc Interface\u003c/em\u003e. 2018;15(141):20170387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, Avison K, Alnassar S, et al. Medical accuracy of artificial intelligence chatbots in oncology: a scoping review. \u003cem\u003eOncologist\u003c/em\u003e. 2025;30(4):oyaf038.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt J, Schutte NM, Buttigieg S, et al. Mapping the regulatory landscape for artificial intelligence in health within the European Union. \u003cem\u003enpj Digit Med\u003c/em\u003e. 2024;7:229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaenz AD, Mass General Brigham AI Governance Committee, Centi A, et al. Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions. \u003cem\u003enpj Digit Med\u003c/em\u003e. 2024;7:348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisemann N, Bunk S, Mukama T, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. \u003cem\u003eNat Med\u003c/em\u003e. 2025;31:917\u0026ndash;924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan YC, Strauch M, Pilva P, et al. Ecologically sustainable benchmarking of AI models for histopathology. \u003cem\u003enpj Digit Med\u003c/em\u003e. 2024;7:378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Enichen EJ, Heydari K, et al. Artificial intelligence guided imaging as a tool to fill gaps in health care delivery. \u003cem\u003enpj Digit Med\u003c/em\u003e. 2025;8:248.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6880540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6880540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of artificial intelligence (AI) into healthcare analytics has led to transformative progress across diagnostics, imaging, and population health. Yet, survival analysis\u0026mdash;a critical domain in clinical outcomes research\u0026mdash;continues to be hindered by complex methodologies and limited accessibility for non-programmers. We introduce \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stai.globalstatsol.com\u003c/span\u003e\u003cspan address=\"https://stai.globalstatsol.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, a web-based platform designed to make survival analysis both accessible and interpretable through a no-code, modular interface. ClinicalStatAI currently supports widely used and validated statistical models, including the Cox Proportional Hazards model, the Weibull model, and the Log-normal model. These models are integrated with an AI interpretation layer powered by GPT-4.1, enabling natural language summaries and transparent diagnostic feedback. Initial validation using simulated datasets demonstrated strong performance and interpretability, with typical processing times under 30 seconds for mid-sized datasets. ClinicalStatAI provides a foundational step toward democratizing real-time health outcomes modeling by aligning statistical robustness with AI-assisted usability.\u003c/p\u003e","manuscriptTitle":"ClinicalStatAI: A Cloud-Based, AI-Augmented Platform for Accessible Survival Analysis in Healthcare","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 06:46:39","doi":"10.21203/rs.3.rs-6880540/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"181bb3f2-c652-4d1f-9589-ba13f8ca74f2","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T06:46:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 06:46:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6880540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6880540","identity":"rs-6880540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}