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Open research questions in Clinical Reasoning and Diagnostic Skills

330 gap statements mined from Clinical Reasoning and Diagnostic Skills papers in our 7.9M-paper local library, which holds 1,793 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • There is limited evidence on how cognitive and system-level factors manifest in routine primary care encounters. There is a need to understand how diagnostic safety concerns arise in primary care.

    Documentation-related diagnostic safety concerns in primary care: a structured medical record review of 1116 patient records · 2026 · DOI

  • Although remediation frameworks exist, little is known about how pediatric residency programs approach CR remediations or the barriers they face.

    Listen to the Learner: A Grounded Theory Approach to Clinical Reasoning Remediation in Pediatric Residency · 2026 · DOI

  • Efforts to improve diagnosis should reflect the unique needs of children; however, there is no consensus on paediatric-specific priority areas.

    Priorities for improving paediatric diagnosis: findings from a modified Delphi study · 2026 · DOI

  • Improving paediatric diagnosis by addressing errors (Safety-I) is insufficient for diagnostic excellence.

    Beyond ‘diagnostic error’: an ethnography of resilient behaviours and system adaptations in paediatric outpatient diagnosis · 2026 · DOI

  • In spite of limited information in the primary voice messages the PCDs stated that the message were an important factor deciding whether to call out.

    Acute trauma alarms in Norway - a qualitative study of factors influencing primary care doctors’ decision to call out · 2026 · DOI

  • PCDs must decide whether to call out based on limited information shortly after a trauma incident.

    Acute trauma alarms in Norway - a qualitative study of factors influencing primary care doctors’ decision to call out · 2026 · DOI

  • Finally, we highlight open questions and emerging directions, with particular focus on the expanding role of human-AI collaboration.

    Collective intelligence in clinical medicine: what works, what fails, and how collaboration with AI really helps · 2026 · DOI

  • The Dunning-Kruger effect is an underrecognized challenge in neurocritical care, where novice clinicians' overconfidence can harm patient outcomes.

    Miscalibrated Confidence as a Patient‑Safety Risk: Recognizing the Dunning–Kruger Effect in the Neuro ICU · 2026 · DOI

  • Future research should investigate different models of generalist-specialist team composition.

    Doing ‘detective work’ to find a cancer: how are non-specific symptom pathways for cancer investigation organised, and what are the implications for safety and quality of care? A multisite qualitative approach · 2025 · DOI

  • Research demonstrates that emotions play an important role in clinical reasoning (CR); however, the relationship between emotional valence, CR, and the context in which reasoning takes place, remains to be empirically explored.

    Physicians have feelings: illuminating the relationship between emotional valence, clinical reasoning and context specificity · 2024 · DOI

  • How ambulance clinicians (ACs) handle a mass casualty incident (MCI) is essential for the suffered, but the training and learning for the ACs are sparse and they don't have the possibility to learn without realistic simulation training.

    Exploring ambulance clinicians’ clinical reasoning when training mass casualty incidents using virtual reality: a qualitative study · 2024 · DOI

  • However, little is known about how to measure safety of clinical diagnosis made through telehealth-facilitated primary care.

    An electronic trigger to detect telemedicine-related diagnostic errors · 2024 · DOI

  • The study had a small sample size of five musculoskeletal experts. The study focused on a specific context (musculoskeletal physiotherapy) and may not be generalizable to other areas. The study used a limited number of case vignettes and clinical reasoning tasks.

    Intelligence without intuition: a mixed-methods pilot study on reasoning models in musculoskeletal physiotherapy for low-back pain · 2026 · DOI

  • Future research should explore how GenAI can enhance advanced clinical judgment and ethical reasoning, thereby promoting sound clinical decision-making, ethical integrity, and professional thoughtfulness, while maintaining key ethical principles. Further investigation into the continued role of faculty oversight in AI-enabled educational settings is also necessary.

    AI-supported case-based learning in medical education: a comprehensive scoping review · 2026 · DOI

  • Further development and refinement of the AI tool to improve its accuracy and address its limitations. Investigation of the use of AI tools in other areas of medical education. Evaluation of the impact of AI tools on student learning outcomes.

    Improving Medical Assessment Validity with Generative AI: Lessons from Human–AI Interaction in Bloom’s Taxonomy Classification · 2026 · DOI

  • The current evidence base for agentic AI in ophthalmology is limited. Hallucination risks in large language models are a challenge. Failures in tool selection or knowledge retrieval are a challenge.

    Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning · 2026 · DOI

  • Therapeutic reasoning is complex and context-dependent. AI models tend to focus on pharmacological recommendations and neglect broader contextual factors. Students need to develop reflective skills to critically appraise AI recommendations.

    Evaluating the Educational Value of Artificial Intelligence in Pharmacotherapy Education · 2026 · DOI

  • The educational value of AI in pharmacotherapy education is unclear. There is a need to evaluate the potential of AI to support therapeutic decision-making and its use as a supplementary educational tool.

    Evaluating the Educational Value of Artificial Intelligence in Pharmacotherapy Education · 2026 · DOI

  • Prior studies have not identified which admission-based factors predict harmful diagnostic errors in hospitalized patients. The study aims to address this gap by examining the association between admission-based predictors and harmful diagnostic errors in high-risk patients.

    Frequent Ambulatory Care Visits Predict Harmful Diagnostic Errors in High-Risk Hospitalized Patients: a Retrospective Cohort Study · 2026 · DOI

  • The lack of real-time data integration and dedicated clinical oversight in diagnostic reconciliation. The limitations of traditional retrospective audits in identifying systemic failures in a timely manner.

    Letter to the Editor: Beyond the audit, real-time data integration and dedicated clinical oversight in diagnostic reconciliation · 2026 · DOI

  • The program may have been suboptimally implemented. Barriers to the program included busy preceptor schedules and lack of standardized teaching materials.

    Effect of structured preceptorship on clinical reasoning of pediatric residents in Indonesia: a randomized controlled trial · 2026 · DOI

  • Our findings provide valuable insights into the potential and limitations of commonly used LLMs in this highly specialized and time-sensitive medical area that would benefit from reliable decision-support and educational tools but remains understudied.

    Assessing the capability of large language models in answering pediatric critical care board-style questions · 2026 · DOI

  • The AI tool has a specific and predictable flaw in differentiating 'Analyze' from 'Understand'. The study used a limited dataset of 200 medical exam items. The study did not provide specific training on interpreting AI to the professors.

    Improving Medical Assessment Validity with Generative AI: Lessons from Human–AI Interaction in Bloom’s Taxonomy Classification · 2026 · DOI

  • Developing more rigorous evaluation frameworks for ophthalmic AI systems. Investigating the potential of agentic AI in ophthalmology. Improving the trustworthiness of ophthalmic AI systems.

    Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning · 2026 · DOI

  • Further investigation into the application of Large Concept Models in diagnostic reconciliation. The development of more advanced digital dashboards to support real-time data visualization and clinical decision-making.

    Letter to the Editor: Beyond the audit, real-time data integration and dedicated clinical oversight in diagnostic reconciliation · 2026 · DOI

Most-cited papers in Clinical Reasoning and Diagnostic Skills

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Open questions raised by papers listed on this page include: (1) There is limited evidence on how cognitive and system-level factors manifest in routine primary care encounters (“Documentation-related diagnostic safety concerns in primary care: a structured…”, 2026); (2) Although remediation frameworks exist, little is known about how pediatric residency programs approach CR remediations or the barriers they face (“Listen to the Learner: A Grounded Theory Approach to Clinical Reasoning…”, 2026); (3) Efforts to improve diagnosis should reflect the unique needs of children; however, there is no consensus on paediatric-specific priority areas (“Priorities for improving paediatric diagnosis: findings from a modified Delphi…”, 2026); (4) The educational value of AI in pharmacotherapy education is unclear (“Evaluating the Educational Value of Artificial Intelligence in Pharmacotherapy…”, 2026); (5) The lack of real-time data integration and dedicated clinical oversight in diagnostic reconciliation (“Letter to the Editor: Beyond the audit, real-time data integration and…”, 2026). 330 gap statements have been mined from Clinical Reasoning and Diagnostic Skills papers in our 7.9M-paper local library, which holds 1,793 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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