Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.

The marked molecular and clinical heterogeneity of ovarian cancer necessitates personalized therapeutic strategies [ 87 , 88 ]. As we know, conventional treatment approaches often rely on standardized protocols or a limited set of biomarkers, resulting in variable outcomes and substantial treatment-related toxicity [ 87 , 89 ]. Artificial intelligence (AI) and machine learning (ML) address these limitations by integrating multi-modal data to predict patient-specific treatment response, toxicity, and disease trajectory, thereby enabling precision oncology tailored to individual patient profiles [ 90 , 91 ]. AI-driven clinical decision support systems (CDSS) integrate diverse patient data, including demographics, comorbidities, laboratory values, pharmacokinetics, imaging, genomic profiles, and prior treatment history, to support individualized therapy selection [ 92 , 93 ]. Unlike traditional trial-and-error approaches, these systems incorporate predictive modeling of both therapeutic efficacy and toxicity, thus assisting clinicians in balancing benefit and risk [ 94 ]. This capability is particularly relevant for patients with advanced age or significant comorbidities, in whom standard chemotherapy regimens may be poorly tolerated. AI-based CDSS can account for renal and hepatic function, cardiovascular status, and cumulative exposure to cytotoxic agents to inform drug selection and dosing, thereby reducing avoidable toxicity without clinical benefit. Importantly, these systems are designed to function as decision-support tools rather than autonomous decision-makers. Explainable AI (XAI) approaches, including SHAP and Grad-CAM, enable transparent visualization of key factors influencing model recommendations, supporting clinician trust, interpretability, and regulatory acceptance [ 95 ]. Radiomics-based prognostic models, including the radiomic prognostic value (RPV) and enhanced RPV (eRPV), combine imaging-derived features with molecular data to stratify patients by risk and predict clinical outcomes [ 49 ]. In patients with bilateral disease, RPV prioritizes the higher-risk lesion, improving prognostic accuracy. Such stratification informs clinical decision-making related to surgical planning, chemotherapy intensity, and clinical trial eligibility. Validation across the cancer genome atlas (TCGA) and multiple independent institutional cohorts has demonstrated reproducibility and generalizability, supporting the feasibility of implementation in multicenter studies and routine clinical workflows [ 96 ]. Advanced deep learning frameworks provide robust tools for modeling complex relationships among patient characteristics, treatment regimens, and outcomes [ 97 – 99 ]. These approaches can capture interactions among multiple therapies and comorbid conditions, facilitating optimization in patients with complex clinical profiles. By incorporating longitudinal clinical data, deep learning models can also characterize temporal patterns in treatment response and adverse events [ 100 , 101 ]. Collectively, these systems generate predictive insights that extend beyond traditional statistical models, supporting more individualized and adaptive treatment strategies [ 102 , 103 ]. Integration of multi-omics datasets, including genomics, transcriptomics, DNA methylation, metabolomics, and proteomics, further enhances the predictive performance of AI-based therapeutic models [ 26 , 104 , 105 ]. Correlation of eRPV-associated gene expression signatures with radiomic features enables refined survival prediction and therapy selection while offering biological insight into tumor behavior [ 15 , 106 ]. Gene set enrichment analysis (GSEA) has identified numerous pathways associated with high-risk RPV scores, highlighting potential therapeutic targets and informing rational combination strategies [ 15 , 105 ]. By integrating clinical, imaging, and molecular data, AI/ML frameworks provide a comprehensive approach to individualized treatment planning, encompassing chemotherapy selection, targeted therapies, and immunotherapeutic strategies [ 107 , 108 ]. Together, these tools represent a clinically relevant shift toward precision oncology in ovarian cancer, supporting actionable, interpretable, and patient-specific therapy decisions while minimizing toxicity and unnecessary healthcare costs [ 54 , 109 ] (Fig. 4 ). Fig. 4 Artificial Intelligence and Machine Learning Approaches for Personalized Therapy in Ovarian Cancer. The figure illustrates the integration of AI/ML techniques, including multi-omics data analysis, radiomics, and advanced neural network architectures to tailor therapeutic strategies specific to individual patients. These models support clinical decision-making by predicting treatment response, optimizing drug selection, and enabling dynamic monitoring of tumor evolution. The application of explainable AI further enhances clinician trust and facilitates the translation of personalized therapy into routine oncology practice Artificial Intelligence and Machine Learning Approaches for Personalized Therapy in Ovarian Cancer. The figure illustrates the integration of AI/ML techniques, including multi-omics data analysis, radiomics, and advanced neural network architectures to tailor therapeutic strategies specific to individual patients. These models support clinical decision-making by predicting treatment response, optimizing drug selection, and enabling dynamic monitoring of tumor evolution. The application of explainable AI further enhances clinician trust and facilitates the translation of personalized therapy into routine oncology practice

Advancing AI and ML in ovarian cancer requires well-designed prospective, multi-center clinical trials to confirm the utility, safety, and effectiveness of AI-supported decision-making [ 130 – 132 ]. While retrospective studies have demonstrated promise in predicting clinical outcomes and treatment-related risks, prospective validation is essential to achieve regulatory approval and clinician acceptance. These trials should evaluate not only overall survival (OS) and progression-free survival (PFS) but also patient-reported outcomes, healthcare utilization, and cost-effectiveness to facilitate meaningful clinical translation [ 42 , 105 , 131 , 133 ]. An emerging area of interest involves integrating longitudinal and wearable-derived data. Continuous monitoring of physiological signals, activity metrics, sleep patterns, and dynamic biomarkers may improve early detection of toxicity, refine response prediction, and enable adaptive, patient-centered treatment adjustments [ 134 , 135 ]. We believe that combining these real-time measures with pharmacokinetic data and CA-125 trajectories holds the potential to further individualize care in the near future. As therapeutic options continue to evolve, AI platforms must accommodate emerging modalities such as immune checkpoint inhibitors, PARP inhibitors, targeted agents, and rational combination therapies [ 136 – 138 ]. Integrating multiomics’ signatures, radiomic features, and longitudinal clinical data can help identify molecularly defined subgroups that are most likely to respond to these treatments. For example, refining PARP inhibitor selection through BRCA mutation status, homologous recombination deficiency (HRD), and radiomic estimates of tumor heterogeneity represents a promising approach [ 139 – 141 ]. The development of scalable, interoperable AI systems with cross-cancer applicability is another important direction [ 117 , 133 , 142 – 146 ]. Standardized data harmonization pipelines, reproducible radiomics workflows, and explainable AI frameworks will be essential to ensure transparency and regulatory readiness across institutions [ 42 , 58 ]. Such systems have the potential to provide consistent, patient-specific recommendations across the continuum of detection, treatment, and prevention, supporting a unified, data-driven precision oncology framework [ 147 , 148 ]. Together, these priorities, prospective validation, real-time patient monitoring, integration of emerging therapies, and scalable multi-cancer AI platforms will be critical for transitioning AI and ML technologies from research concepts into routine clinical tools in ovarian cancer care [ 117 , 133 , 142 – 146 ].

Despite the substantial promise of artificial intelligence (AI) and machine learning (ML) in ovarian cancer care, several challenges continue to limit their widespread clinical adoption. Data heterogeneity remains a primary obstacle. Ovarian cancer datasets are typically generated across institutions using heterogeneous imaging protocols, laboratory assays, and electronic health record (EHR) systems. Variability in CT and MRI acquisition parameters, biomarker platforms, and genomic sequencing pipelines introduces systematic bias that can undermine model reproducibility and generalizability [ 122 , 123 ]. Inconsistent clinical annotations, missing data, and demographic variability further tend to complicate model development. Addressing these limitations requires standardized radiomics workflows, harmonized metadata, and rigorous data curation to ensure that AI models retain performance across independent cohorts. We opine that clinical validation represents an equally important challenge. Most AI/ML studies in ovarian cancer remain retrospective and single-center in design [ 35 , 124 – 126 ]. Without prospective, multi-institutional validation, it remains difficult to demonstrate meaningful improvements in survival, toxicity reduction, workflow efficiency, or cost-effectiveness in real-world practice. Therefore, embedding AI evaluation within multicenter clinical studies is essential to assess generalizability across diverse patient populations and to generate evidence required for regulatory approval and clinician confidence [ 127 ]. Regulatory, ethical, and implementation considerations further shape clinical feasibility. Regulatory agencies, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), increasingly emphasize transparency, data provenance, fairness, and reproducibility in AI-enabled clinical tools [ 128 , 129 ]. Models trained on imbalanced datasets may unintentionally encode bias, potentially exacerbating disparities in care. Explainable AI (XAI) approaches, including SHAP, LIME, Grad-CAM, and GNN Explainer, support model auditing, bias detection, and clinician interpretability, thereby facilitating ethical and equitable deployment. From an operational perspective, integration into existing clinical workflows remains challenging. Many institutions rely on legacy EHR and imaging systems with limited interoperability, and AI tools must deliver clear, actionable outputs rather than abstract risk scores to be clinically useful. Despite these barriers, important opportunities exist to accelerate translation outcomes. It is anticipated that federated learning will enable privacy-preserving, multi-institutional model training without centralized data sharing, addressing both regulatory and data-access constraints. Standardized radiomics and multi-omics pipelines can improve reproducibility across sites, while human-in-the-loop frameworks can position AI as a decision-support aid rather than a replacement for clinician judgment. Together, these advances provide a realistic pathway for transitioning AI from experimental models to scalable, ethically robust, and clinically actionable tools in ovarian cancer care [ 120 , 127 ]. Continued coordination across institutions, regulators, and clinical stakeholders will be critical to fully realizing the potential of AI-driven precision oncology.

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming ovarian cancer care by integrating genomic, radiomic, clinical, and lifestyle data to generate individualized insights that improve early detection, risk stratification, and treatment selection [ 45 , 149 – 151 ]. These platforms capture complex molecular and phenotypic patterns that often elude traditional assessments, thereby enhancing treatment precision while minimizing toxicity [ 15 , 91 , 132 , 140 , 152 ]. Radiomics-based models such as the Radiomic Prognostic Value (RPV) and enhanced RPV (eRPV) further illustrate the utility of quantitative imaging in predicting overall survival (OS) and progression-free survival (PFS) in high-grade serous ovarian cancer (HGSOC) [ 19 , 153 ]. Advanced AI methodologies, including graph neural networks (GNNs), transformer architectures, and explainable AI (XAI) frameworks, enhance multi-modal data integration while maintaining interpretability crucial for clinical adoption [ 154 , 155 ]. Progress in this field will depend on assembling large, multi-institutional datasets, developing standardized data pipelines, and conducting robust prospective validation across diverse patient populations [ 11 , 90 , 113 , 118 , 156 , 157 ]. Additionally, longitudinal and wearable-derived data are poised to support adaptive treatment strategies and facilitate early toxicity detection [ 158 , 159 ]. Further, AI also offers significant translational opportunities by informing adaptive clinical trial design, predicting therapeutic responses, and accelerating the evaluation of novel agents [ 160 – 163 ]. Continuous-learning AI systems have the potential to iteratively refine predictions as new data emerge, linking computational outputs with underlying biological mechanisms to improve survival and quality of life [ 91 , 164 ]. As validated AI tools are integrated into clinical workflows, they will streamline therapeutic decision-making, reduce inefficiencies in drug development, and promote patient-centered care [ 165 – 167 ]. Ultimately, sustained cross-disciplinary collaboration among clinicians, data scientists, and regulatory bodies will be essential to translating these advances into durable clinical impact [ 168 ]. The integration of AI with multi-modal data and precision oncology lays the foundation for a more effective, scalable approach to ovarian cancer management [ 169 , 170 ].

Beyond therapy selection and prognostication, artificial intelligence (AI) and machine learning (ML) together hold increasing promise in ovarian cancer prevention and risk stratification. Early-stage ovarian cancer is frequently asymptomatic, and conventional serum biomarkers, including CA-125 and HE4, demonstrate limited sensitivity and specificity, particularly in premenopausal women and patients with low-volume disease [ 110 , 111 ]. By integrating genetic, clinical, and lifestyle data, we surmise that AI-based models can help identify individuals at elevated risk, guide preventive interventions, and support personalized surveillance strategies [ 112 , 113 ]. Machine learning algorithms are well-suited to capture complex interactions among genetic, reproductive, environmental, and lifestyle factors associated with ovarian cancer risk. Models incorporating germline mutations, family history, reproductive variables (age at menarche, parity, oral contraceptive use, etc.), hormone exposure, body mass index (BMI), and environmental factors can generate individualized lifetime risk estimates [ 114 ]. These risk predictions can inform clinical decision-making related to prophylactic bilateral salpingo-oophorectomy, chemoprevention, or intensified surveillance, allowing high-risk individuals to benefit from early intervention while limiting unnecessary procedures in lower-risk populations [ 113 ]. Importantly, AI-based risk models can be updated dynamically as new patient information becomes available, including changes in lifestyle, comorbidities, or laboratory findings, thereby maintaining longitudinal accuracy and clinical relevance [ 115 ]. However, the clinical application of such models requires careful validation to ensure that risk thresholds are clinically meaningful and do not inadvertently promote overtreatment. At the population level, AI-enhanced screening approaches integrate imaging, biomarker panels, and ML-based risk calculators to improve early detection. Radiomics applied to transvaginal ultrasound, CT, or MRI can quantify subtle morphologic and textural features associated with malignancy, while multi-marker panels, including CA-125 and HE4, improve diagnostic performance [ 56 , 116 ]. Machine learning frameworks combine these data with demographic characteristics, longitudinal laboratory trends, and prior imaging to generate individualized, time-dependent risk profiles. Such adaptive screening models enable variable surveillance intervals, prioritizing high-risk individuals for closer monitoring while reducing unnecessary testing in low-risk populations [ 117 ]. So far, early evidence suggests that AI-guided screening may, in fact, enhance early detection, particularly when implemented within multi-modal pipelines that integrate imaging, molecular, and clinical data points [ 54 ]. Nevertheless, we believe that the potential benefits of AI-based screening must be balanced against possible risks of false positives, overdiagnosis, psychological burden, and inequitable access to advanced screening technologies. Ethical considerations are therefore central to the implementation of AI-driven prevention strategies. Transparent risk communication, population-specific validation, and explainable AI frameworks are essential to support shared decision-making and clinician trust. As prospective validation studies mature, AI-based prevention and risk stratification platforms will become integral to precision ovarian oncology, provided that their deployment is guided by clinical evidence, ethical safeguards, and health system readiness [ 118 , 119 ].

Explainable artificial intelligence (XAI) is a critical prerequisite for the clinical adoption of artificial intelligence (AI) and machine learning (ML) in oncology. Although advanced models, including deep neural networks, transformer architectures, and graph neural networks (GNNs), demonstrate strong predictive performance across imaging, genomics, and drug response modeling, their limited interpretability remains a major barrier to routine clinical use [ 17 ]. In ovarian cancer, where treatment decisions directly affect survival, toxicity, and quality of life, clinicians and regulators require transparent, auditable decision-support tools rather than opaque “black-box” systems. Interpretability is therefore crucial for establishing clinical trust, ensuring safety, and maintaining accountability. Established XAI techniques offer practical mechanisms for interpreting complex models. SHAP (SHapley Additive exPlanations) values quantify the contribution of individual input features, such as tumor burden, renal function, BRCA mutation status, or prior treatment response, hence enabling clinicians to understand the factors driving model predictions [ 120 ]. Grad-CAM (Gradient-weighted Class Activation Mapping) highlights image regions most influential in AI-based imaging predictions, allowing radiologists to confirm that models focus on biologically and pathologically relevant features rather than artifacts [ 121 ]. For graph-based models, GNN Explainer identifies the nodes and relationships most responsible for predictions, supporting interpretability in multi-modal clinical and molecular networks. Together, these approaches allow clinicians to critically assess AI outputs and align them with established clinical reasoning. The integration of XAI into ovarian cancer workflows also addresses regulatory, ethical, and medico-legal considerations. Regulatory bodies, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), increasingly emphasize transparency, reproducibility, and bias mitigation in AI-enabled medical tools. Without explainability, AI models risk perpetuating hidden biases related to demographic, socioeconomic, or comorbidity-related factors. XAI enables systematic auditing, bias detection, and continuous performance monitoring, supporting regulatory compliance and equitable clinical use. From a medico-legal perspective, explainable systems strengthen shared decision-making by allowing clinicians to trace recommendations back to identifiable data inputs, thereby reducing uncertainty and liability concerns. Importantly, XAI facilitates clinician engagement rather than automation-driven replacement of clinical judgment. When oncologists can clearly interpret why an AI model prioritizes a specific therapy based on molecular features, toxicity risk, and prior response, AI is more readily accepted as a decision-support aid. This transparency promotes interdisciplinary collaboration among oncologists, radiologists, pathologists, and data scientists, enabling iterative refinement and validation of AI systems within real-world clinical settings. Hybrid approaches that combine deep learning with interpretable rule-based or probabilistic frameworks further enhance usability without compromising predictive accuracy. Collectively, the incorporation of XAI is essential to transition AI from a promising research tool into a clinically actionable and trustworthy component of precision ovarian cancer care.

Cancer remains a leading cause of morbidity and mortality globally, and among gynecologic malignancies, ovarian cancer stands out for its particularly high lethality [ 1 , 2 ]. By the end of 2025, it is estimated that over 2 million new cancer cases will be diagnosed in the United States, with more than 600,000 deaths [ 3 ]. Despite accounting for only approximately 4% of all cancers in women worldwide, ovarian cancer carries disproportionately high mortality, primarily due to late-stage presentation and limited precision in therapy selection [ 4 ]. High-grade serous ovarian cancer (HGSOC), the most common histologic subtype, is characterized by extensive molecular heterogeneity, aggressive clinical behavior, and frequent development of chemoresistance, hence complicating both primary and recurrent treatment strategies [ 5 , 6 ]. As we know that conventional clinical decision-making often relies on limited variables, such as tumor size, histology, or single-gene mutation status, most notably BRCA1/2 mutations or homologous recombination deficiency (HRD) to guide therapy [ 7 – 9 ]. However, these metrics fail to capture the full spectrum of factors influencing patient outcomes, including comorbidities, organ function, pharmacokinetics, and lifestyle or behavioral determinants. As a result, treatment regimens often follow a “trial-and-error” approach, thus exposing patients to ineffective therapies, increasing toxicity, and contributing to substantial healthcare costs [ 10 – 13 ]. Artificial intelligence (AI) and machine learning (ML) offer the potential to address these limitations by integrating multi-modal datasets and generating personalized, evidence-informed clinical insights [ 14 ]. For example, radiomics, which involves the extraction of high-dimensional quantitative features from medical imaging, has been increasingly applied in ovarian cancer to characterize tumor heterogeneity, correlate imaging phenotypes with molecular profiles, and predict clinical outcomes [ 15 – 17 ]. Radiomics-based prognostic value (RPV) and enhanced RPV (eRPV) models have truly demonstrated the ability to stratify HGSOC patients into distinct risk groups and predict overall survival (OS) and progression-free survival (PFS), while accounting for complex tumor architecture [ 18 – 21 ]. These imaging-derived models have also been correlated with molecular features such as BRCA mutation status and HRD, thereby offering clinically relevant insights for therapy selection, including platinum-based chemotherapy and PARP inhibitors [ 22 – 25 ]. Beyond imaging, the integration of genomic, transcriptomic, and epigenetic data with clinical and pharmacologic information has shown promise in refining personalized treatment strategies [ 26 – 28 ]. Copy-number alterations, gene expression subtypes, and methylation patterns can be incorporated into predictive frameworks, enabling more nuanced assessment of chemoresistance and therapeutic response. Deep learning architectures, including convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer-based architectures (TAs), have clearly demonstrated utility in modeling these complex, multi-layered datasets [ 29 – 37 ]. GNNs facilitate representation of relational clinical and molecular data, while transformer architectures capture temporal and spatial dependencies within longitudinal imaging and clinical records [ 38 – 41 ]. Explainable artificial intelligence (XAI) approaches, such as SHAP, Grad-CAM, and GNN Explainer, further enhance model transparency by enabling clinicians to interpret features driving predictions, a critical requirement for trust and adoption in oncology practice [ 42 , 43 ]. Growing clinical evidence supports the relevance of AI/ML-driven approaches for improving outcome prediction in ovarian cancer. Multi-modal studies integrating imaging, genomics, and clinical data have demonstrated improved accuracy in predicting treatment response, toxicity risk, and recurrence compared with traditional clinical metrics alone [ 26 , 44 , 45 ]. Again, radiomics-based models linked to BRCA status and HRD have shown potential to inform PARP inhibitor eligibility and anticipate chemoresistance [ 46 , 47 ]. In addition, AI-assisted analysis of longitudinal clinical data, including laboratory parameters, pharmacokinetic profiles, and lifestyle factors, enables dynamic disease monitoring and earlier detection of progression or adverse events [ 15 , 48 ]. This review provides an overview of AI and ML applications in ovarian cancer across three interconnected domains: (i) early detection and risk stratification; (ii) personalized therapy selection and response prediction; and (iii) preventive strategies with longitudinal monitoring. We further discuss the integration of multi-modal platforms, including RPV/eRPV models, GNNs, and transformer-based approaches, along with current challenges, translational considerations, and pathways for clinical adoption (Fig. 1 ) [ 49 , 50 ]. Fig. 1 Overview of AI/ML applications in ovarian cancer care. The schematic illustrates key domains where artificial intelligence (AI) and machine learning (ML) impact ovarian cancer management, including early detection, risk stratification, personalized therapy selection, and preventive strategies. The integration of multi-modal data, such as imaging, genomics, clinical records, and lifestyle factors, enables predictive modeling to support clinical decision-making, optimize treatment outcomes, and improve patient prognosis Overview of AI/ML applications in ovarian cancer care. The schematic illustrates key domains where artificial intelligence (AI) and machine learning (ML) impact ovarian cancer management, including early detection, risk stratification, personalized therapy selection, and preventive strategies. The integration of multi-modal data, such as imaging, genomics, clinical records, and lifestyle factors, enables predictive modeling to support clinical decision-making, optimize treatment outcomes, and improve patient prognosis