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Open research questions in Machine Learning in Healthcare

333 unresolved questions extracted from the limitations and future-work sections of 760 Machine Learning in Healthcare papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Further research on the application of Actuarial NAM in other domains, - Investigation of other methods to ensure monotonicity, - Exploration of other techniques to reduce computational cost

    An Interpretable Deep Learning Model for General Insurance Pricing · 2026 · DOI

  • The lack of interpretability in deep learning models for general insurance pricing, - The need for a model that offers transparent and interpretable results while retaining strong predictive power, - The limitations of traditional actuarial models in capturing complex relationships between variables

    An Interpretable Deep Learning Model for General Insurance Pricing · 2026 · DOI

  • Data scarcity, - Domain heterogeneity, - Limited interpretability, - Lack of prospective outcome-based validation, - Unverified generalization beyond over-simplified benchmark settings

    Foundation models in biomedical imaging: turning hype into reality · 2026 · DOI

  • Coordinated subspecialist AI systems that are transparent, safe, and clinically grounded, - Development of FMs that can reason about anatomy, disease progression, and treatment response beyond statistical pattern matching

    Foundation models in biomedical imaging: turning hype into reality · 2026 · DOI

  • further research on empirical prompt testing, - investigation of the applicability of this prompting strategy to other clinical domains, - development of more advanced prompt engineering techniques, - exploration of the use of LLMs in other data extraction tasks

    Developing a scalable pipeline for data extraction from clinical letters through resource-efficient prompt engineering · 2026 · DOI

  • The lack of a scalable, systematic, and automated approach to data extraction from clinical letters. The limitations of traditional supervised machine learning and rule-based systems for data extraction. The need for a resource-efficient and high-performance pipeline for data extraction.

    Developing a scalable pipeline for data extraction from clinical letters through resource-efficient prompt engineering · 2026 · DOI

  • further development of SAFE-AI to address other clinical applications, - investigation of SAFE-AI in other medical domains

    A medically grounded LLM agent–based tool to detect patient safety events in medical records · 2026 · DOI

  • The gap in current methods is the tendency of LLMs to hallucinate and fabricate information, which can be misleading and dangerous in clinical settings. The gap is also the lack of a reliable and accurate method for detecting patient safety events using LLMs. The paper identifies the need for a method that combines the strengths of clinical expert knowledge with LLMs to minimize hallucinations.

    A medically grounded LLM agent–based tool to detect patient safety events in medical records · 2026 · DOI

  • Live birth is a complex multifactorial long-term outcome affected by many maternal variables not recorded in routine clinical data, - Predictive accuracy is inherently limited and results in moderate AUC values, - The study was retrospective and anonymized

    Development and external validation of a three-stage model to predict live birth after fresh IVF/ICSI embryo transfer · 2026 · DOI

  • Most existing models are limited to single time-point data and lack integration of sequential clinical information. Traditional prediction tools are limited by subjectivity, linear assumptions, and an inability to capture complex interactions. Live birth after IVF/ICSI depends on sustained implantation and multiple maternal variables not recorded in routine clinical data.

    Development and external validation of a three-stage model to predict live birth after fresh IVF/ICSI embryo transfer · 2026 · DOI

  • Findings are limited to the studied population and may require external validation for broader applicability, - Model performance may be affected by dataset imbalance, - Performance may be limited by small sample sizes and requires careful handling

    Collaborative deep neural network for survival prediction of hepatitis patients using electronic health records · 2026 · DOI

  • The increasing volume of data in EHRs means they are becoming more valuable for data-driven healthcare research. Sophisticated analytical techniques are progressively required to capture meaningful patterns and support predictive modeling. Recent advances in deep learning (DL) techniques have demonstrated the capability to learn feature representations from data and enhance model performance across diverse fields.

    Collaborative deep neural network for survival prediction of hepatitis patients using electronic health records · 2026 · DOI

  • The need for a validated predictive engine that can provide personalized predictions. The lack of a Mother-Child AI agent that can predict disease risks for mothers and infants.

    Prediction of maternal and infant outcomes from longitudinal electronic health records with a Mother-Child AI agent · 2026 · DOI

  • Privacy concerns regarding the use of sensitive patient data. Liability issues related to the use of AI-based tools. The need for a high level of encryption to protect patient data.

    Artificial intelligence is going to transform the field of endocrinology: an overview · 2025 · DOI

  • Privacy concerns regarding the use of sensitive patient data, - Certain regulations have to be adhered to, - The issue of liability remains unclear, - The use of AI models can be complicated and inhibited in some situations

    Artificial intelligence is going to transform the field of endocrinology: an overview · 2025 · DOI

  • We were limited by the scope of data in this initial study and focused on openly available datasets.

    Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes · 2026

  • These results demonstrate that correctness assessment is an integral component of clinical UE evaluation and should be validated before UE methods are compared.

    Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models · 2026

  • Prior work has localized EM in model weights, activations, and training documents, but it remains unclear which training tokens carry the relevant fine-tuning signal.

    TAME: Token Attribution and Masking for Emergent misalignment · 2026

  • These findings suggest that while classical ensemble methods remain difficult to outperform on tabular thyroid data, both classical and deep learning models exhibit a clinically meaningful age-based bias that warrants attention before deployment in screening contexts.

    Deep Learning-Based Thyroid Disease Prediction with Fairness-Aware Evaluation: A Comparative Study Against Classical Machine Learning Approaches · 2026 · DOI

  • Furthermore, the efficacy of metaheuristic optimization in stabilizing these complex architectures remains systematically under-investigated.

    HHO-optimized VAE-transformer framework for robust clinical phenotyping and EHR reconciliation in diabetes management · 2026 · DOI

  • The clinician must reconcile conflicting evidence, weigh competing risks, and factor in social constraints.

    Cognitive alignment in cardiovascular AI: designing predictive models that think with, not just for, clinicians · 2025 · DOI

  • Future research will focus on enhancing scalability and interpretability for broader clinical applications.

    Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care · 2025 · DOI

  • Current challenges arise from the limited inclusion of structured SDoH information within electronic health record (EHR) systems, often due to the lack of standardized diagnosis codes.

    On the development and validation of large language model-based classifiers for identifying social determinants of health · 2024 · DOI

  • Although a large number of computational methods are designed to screen novel microbe-disease associations, the accurate and efficient methods are still lacking due to data inconsistence, underutilization of prior information, and model performance.

    Predicting microbe–disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion · 2024 · DOI

  • Traditional clinical decision support systems often rely on rule-based approaches that struggle to effectively integrate and interpret complex multimodal data. The lack of a holistic, interpretable, and scalable solution for clinical decision support.

    ARTIFICIAL INTELLIGENCE DRIVEN CLINICAL DECISION SUPPORT SYSTEMS: OPPORTUNTIES, FUTURE AND DIRECTIONS · 2026 · DOI

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333 open questions have been extracted from the limitations and future-work passages of 760 Machine Learning in Healthcare papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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