WITHDRAWN: Causal Workflow AI: Learning Clinical Care Pathways for Safe and Trustworthy Decision Support
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This withdrawn Research Square preprint titled “Causal Workflow AI: Learning Clinical Care Pathways for Safe and Trustworthy Decision Support” concerns an AI approach intended to learn clinical care pathways for decision support, authored by Mahule Roy and Omar Abudayyeh. However, the only content provided in the paper text is an editorial withdrawal notice indicating the submission was retracted because it was submitted without the consent/knowledge of all listed authors and included inaccurate contact information for one or more authors. The paper therefore does not provide usable scientific methods, results, or limitations beyond the administrative withdrawal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract Artificial intelligence has made impressive progress in healthcare, yet real-world clinical adoption remains limited due to a fundamental disconnect between static prediction models and dynamic clinical workflows. Current systems fail to capture the sequential nature of medical decision making, lack causal understanding of treatment effects, and provide recommendations that often violate clinical safety constraints. This leads to clinician mistrust and limited practical utility. We introduce CausalCare, a comprehensive framework that bridges this gap through integrated causal inference, temporal modeling, and explicit safety validation. Our approach learns clinical care sequences from multimodal EHR data by constructing causal workflow graphs that respect temporal precedence, incorporate domain knowledge, and enforce safety constraints through a multi-layered validation system. CausalCare provides transparent, step-level explanations aligned with clinical reasoning patterns through three complementary mechanisms: causal pathway visualization, safety rationale presentation, and temporal context analysis. Extensive validation across four large-scale clinical datasets (MIMIC-IV, eICU, OMOP-CDM, MIMIC-CXR) demonstrates superior performance in predicting clinically appropriate next actions (F1-score: 0.81 vs 0.76 best baseline) while reducing unsafe recommendations by 69% compared to state-of-the-art baselines. Our framework achieves particular strength in complex scenarios requiring causal understanding, such as medication sequencing and diagnostic test ordering. A comprehensive ablation study confirms the synergistic contributions of causal learning, safety constraints, and temporal modeling, with the integrated framework outperforming individual components by 11-16%. Through detailed case studies in sepsis management, heart failure, and depression treatment, we demonstrate CausalCare’s ability to respect clinical sequencing, avoid contraindications, and provide interpretable decision support. The framework establishes a new paradigm for trustworthy clinical AI that aligns with human reasoning, workflow patterns, and safety imperatives, representing a significant advancement toward clinically deployable decision support systems.
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WITHDRAWN: Causal Workflow AI: Learning Clinical Care Pathways for Safe and Trustworthy Decision Support | 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 WITHDRAWN: Causal Workflow AI: Learning Clinical Care Pathways for Safe and Trustworthy Decision Support Mahule Roy, Omar Abudayyeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8413099/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Editorial Note 14 May, 2026. Research Square has withdrawn this preprint following findings that it was submitted without the consent or knowledge of all listed authors, and that inaccurate contact information was provided for one or more authors. The submission was therefore withdrawn for non-compliance with our authorship and submission requirements. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract 14 May, 2026. Research Square has withdrawn this preprint following findings that it was submitted without the consent or knowledge of all listed authors, and that inaccurate contact information was provided for one or more authors. The submission was therefore withdrawn for non-compliance with our authorship and submission requirements. Biomedical Engineering Full Text 14 May, 2026. Research Square has withdrawn this preprint following findings that it was submitted without the consent or knowledge of all listed authors, and that inaccurate contact information was provided for one or more authors. The submission was therefore withdrawn for non-compliance with our authorship and submission requirements. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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