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Open research questions in Clinical Reasoning and Diagnostic Skills

81 unresolved questions extracted from the limitations and future-work sections of 976 Clinical Reasoning and Diagnostic Skills papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Foundations & trends in multimodal machine learning: Principles, challenges, and open questions. DRPO, our reinforcement learning algorithm for training on highly heterogeneous data, brings the most performance gain in understudied modalities as defined in Method Section.

    QoQ-Med3: a multimodal reasoning foundation model for clinical analysis · 2026 · DOI

  • Results: The “See it, Address it, Solve it” model illustrates how generalist practitioners move through three iterative phases: identifying gaps in knowledge or care (“See it”), engaging in innovative and context-specific problem-solving (“Address it”), and implementing and refining solutions using clinical judgement (“Solve it”).

    See it, Address it, Solve it” framework to support understanding and facilitate development of adaptive expertise in training and practice · 2026 · DOI

  • Abstract Background Artificial intelligence–generated health information is increasingly used by patients, but its reliability, visible transparency indicators, and readability remain uncertain in specialized ophthalmic conditions such as age-related macular degeneration (AMD).

    Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study · 2026 · DOI

  • Abstract Background Large language models (LLMs) and large reasoning models (LRMs) have shown excellent performance on medical benchmarks, although evaluations concerning real-world medical workflows are still lacking.

    Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study · 2026 · DOI

  • Had we known earlier, Jeff’s diagnostic path—and his potential trial eligibility—might have been different. Jeff’s story is not just about loss. It is about what his journey revealed: that clinical trajectory still matters; that caregiver observations are often the earliest and most consistent evidence; that biomarkers, while powerful, cannot stand alone; and that when results conflict, we need systems that integrate rather than privilege individual signals. Neurodegenerative disease demands a different diagnostic paradigm— one that integrates advancing imaging, evolving biofluid biomarkers, emerging automated algorithms, clinical expertise, and lived experience into a unified model of disease2, with each component interpreted in the context of the individual’s clinical presentation, including the perspectives of the person and their caregivers. Earlier use of biofluid biomarkers may help reduce prolonged diagnostic delay, but the accuracy of these evolving tools must be confirmed through neuropathological validation, and discordant data must be understood and reconciled. Not a single answer, but an ecosystem. The longitudinal evolution of symptoms, biomarker findings, diag- nostic reinterpretations, and eventual autopsy-confirmed pathology is summarized in Fig. 2.

    What the tests missed: a journey through misdiagnosis · 2026 · DOI

  • Fig. 2 | Longitudinal clinical, biomarker, and diagnostic evolution culminating in autopsy-confirmed corticobasal degeneration (CBD). This timeline illustrates the progression from late-onset psychiatric symptoms to severe corticobasal syndrome, highlighting how evolving clinical features, imaging, fluid biomarkers, and α-synuclein assays produced shifting and at times conflicting diagnostic interpretations. Despite extensive testing, the underlying 4R tauopathy was only definitively established at autopsy, underscoring the limitations of current biomarker frameworks and the need for more integrated multimodal diagnostic frameworks.

    What the tests missed: a journey through misdiagnosis · 2026 · DOI

  • But each answer shifted. Jeff’s case was not defined by error, but by the limits of interpreting emerging biomarkers without sufficient autopsy validation or integration with clinical trajectory. In 2023, everything accelerated. Jeff began needing help with basic functions—dressing, eating, toileting, and even walking safely outside the house. His brain could no longer reliably identify objects. He struggled to distinguish a chair from a table, or a footstool, or even a person. I was constantly on guard, making sure he did not try to sit where it was unsafe. I followed him with a wheelchair. In the shower, Jeff could only wash one part of his body, yet believed he had washed it all. He brushed only the right side of his teeth, unaware of the left. He would ask me if he was sitting while still walking. At times, his right hand seemed to act independently, grasping his body and refusing to let go —a manifestation of alien limb. He was completely unaware of it. Meanwhile, I became his arms. His eyes. His safety net. Under strain, my own body gave out. I had a heart attack. My cardiologist urged me to place Jeff in a care home. We tried. But when I saw the pain in his eyes as we toured the facility, I could not do it. I cried the entire drive home. Throughout his illness, I noticed something the data did not fully capture. His FDG-PET scans showed only modest progression, yet his functional decline was profound4. Because the scan progression appeared modest, one neurologist told us in 2023 that Jeff could live another five years, arguing that imaging predicted longevity more reliably than clinical symptoms. I was stunned—Jeff was declining rapidly before my eyes in ways the scans did not capture. Another neurologist disagreed. Prognosis, he said, was determined by clinical trajectory, not imaging. Jeff was at a high risk for falls or swallowing complications. He was right. Six months later, we could no longer manage at home. Two weeks before moving to a care facility, Jeff asked repeatedly to use his exercise bike one last time. I hesitated, balancing safety against his need for autonomy. That day, I chose to honor him. I helped him downstairs and onto the bike. It went smoothly. But on the way back up, in an instant, he lost the memory of how to move his feet. He stepped into empty space. I watched in horror as he fell down seven steps. He shattered his pelvis, broke multiple ribs, and his arm. I implored three different surgeons to fix his pelvis to alleviate his pain, but his bones were too fragile to repair. After weeks of unrelenting pain, he repeatedly expressed the need for relief that was not adequately addressed in the longterm care setting. With a tear rolling down his cheek, he said, “It has always been my strategy to choose death over pain.” As his condition declined, he became unable to swallow.

    What the tests missed: a journey through misdiagnosis · 2026 · DOI

  • Figure 1 formalizes the three-layer model described above and frames cross-silo integrative reasoning as the educational objective of the Uncertainty Layer. To clarify this dynamic, we propose the following three-layer model of clinical reasoning: 1. Evidence Layer: scientific evidence organized through EBP frameworks and guidelines. 2. Schema Layer: internalized knowledge structures enabling heuristic decision-making. 3. Uncertainty Layer: explicit reasoning processes required when schemas become insufficient. The framework proposed in this manuscript targets the third layer. By introducing Bayesian reasoning as an epistemic process and operationalizing it through structured simulators, the model aims to transform implicit expert reasoning under uncertainty into an explicit educational objective. At an operational level, the model uses large language models (LLMs) as epistemic tools rather than productivity aids. The framework is implemented through support progression from correct execution within established schemas to explicit judgment under unresolved uncertainty. Within these simulators, LLMs are deliberately constrained as adversarial interlocutors, serving not to provide answers but to challenge reasoning.

    Reasoning under uncertainty in graduate health education: a scaffolded framework using large language models · 2026 · DOI

  • However, it leaves a critical gap when learners encounter cross-silo problems in which guideline- derived recommendations do not converge, evidence is sparse or conflicting, or the decision context extends beyond the scope of inference supported by stabilized studies.

    Reasoning under uncertainty in graduate health education: a scaffolded framework using large language models · 2026 · DOI

  • The framework presented here has several limitations that should be acknowledged. First, and most consequentially, the framework remains theoretical. It has not been empirically reasoning validated. Whether transparency, under uncertainty, or transfer across cases and domains is an open empirical question that requires dedicated study. The conceptual coherence of the framework is not a substitute for evidence of its educational efficacy. simulators integration, improve calibration cross-silo the Second, LLM performance is model-, prompt-, and contextdependent. The framework specifies the LLM’s adversarial role through constrained prompting, but model behavior remains variable across model versions, vendors, and clinical domains. Even with careful prompt design, LLMs can express uncertainty about their outputs while exhibiting overconfidence in incorrect answers (Madrid et al., 2025). The framework treats this as a feature for advanced learners (i.e., an additional opportunity to practice epistemic discrimination) but for less experienced learners, spurious LLM challenges may go unrecognized and reinforce flawed reasoning. Faculty oversight is therefore not an the optional framework’s safe and effective use. structural condition of supplement but a Third, the cross-frame literacy condition discussed above is non-negotiable. Learners who lack sufficient outsider-discipline literacy cannot meaningfully engage cross-silo simulators. Pathways to acquire foundational literacy in an unfamiliar discipline, such as the structured-comprehension and schemaconsolidation simulators in Ugrinowitsch and Libardi (preprint), should be considered prerequisites rather than complements when curriculum. Implementing the framework without addressing this asymmetry will produce well-articulated reasoning that nonetheless defends crossrather predetermined silo integration. cross-silo positions genuine central cases than the are to Fourth, the non-normative rubric for longitudinal monitoring requires faculty calibration before it can be used reliably across instructors. Inter-rater reliability has not been established, and time should be in rubric ratings over observed changes interpreted with appropriate caution about whether they reflect genuine reasoning development or familiarity with the simulator format. The rubric is offered for developmental tracking, not for summative assessment, and using it for high-stakes evaluation without prior validation work would be premature.

    Reasoning under uncertainty in graduate health education: a scaffolded framework using large language models · 2026 · DOI

  • In this tutorial, we highlight the underrecognized concept of adverse effects in medical education by introducing 12 representative educational adverse effects and offering corresponding tips for mitigating them.

    Twelve Tips for Recognizing and Addressing the Adverse Effects of Medical Education Interventions: Tutorial · 2026 · DOI

  • Quantitative validation of the Condition-Decoction system's claim of accessibility for inexperienced practitioners is absent; no studies measure diagnostic accuracy, treatment outcomes, or learning curves comparing experienced versus inexperienced physicians using this systematic scanning model versus other Jinchal approaches.

    Clinical reasoning in traditional medicine exemplified by the clinical encounter of Korean medicine · 2021 · DOI

  • The Decoction-Pattern system's reliance on comprehensive pattern recognition from historical texts (Shanghanlun) lacks systematic documentation of how physicians actually manage complex multi-symptom presentations when pattern coverage in textbooks is incomplete or symptoms map to competing/overlapping diagnostic categories.

    Clinical reasoning in traditional medicine exemplified by the clinical encounter of Korean medicine · 2021 · DOI

  • Cross-cultural and interdisciplinary comparative studies of clinical reasoning processes between Korean medicine and other indigenous medicine systems have not been conducted, limiting understanding of how different traditional East Asian medical systems operationalize pattern identification and diagnostic decision-making.

    Clinical reasoning in traditional medicine exemplified by the clinical encounter of Korean medicine · 2021 · DOI

  • The paper identifies that Jinchal processes vary across different traditional Korean medicine schools, but lacks empirical data characterizing the specific perceptual differences and cognitive heuristics that practitioners from different schools use when identifying constitutional types and matching symptoms to patterns.

    Clinical reasoning in traditional medicine exemplified by the clinical encounter of Korean medicine · 2021 · DOI

  • Qualitative research methods using situated cognitive approaches (video-recorded clinical observations in naturalistic clinic/hospital environments) have not yet been applied to Korean medicine (KM) clinical settings, despite their demonstrated utility in capturing individualized clinical reasoning data through 'think aloud' and retrospective protocols.

    Clinical reasoning in traditional medicine exemplified by the clinical encounter of Korean medicine · 2021 · DOI

  • OBJECTIVES: Oral case presentation (OCP) is recognized as a central educational and patient care activity, yet has not been well studied in the emergency medicine (EM) setting.

    Emergency Medicine Oral Case Presentations: Evaluation of a Novel Curriculum · 2019 · DOI

  • Further studies of efficacy and effect of CBL on the quality of education require coordination concerning methodological terminology and definitions, creation of a powerful interdisciplinary base (electronic and printed), including clinical cases from “simple” to more “complicated”.

    TOPICALITY OF IMPLEMENTATION OF THE MODEL «CASE-BASED LEARNING» (CBL) SYSTEM IN PROFESSIONAL TRAINING AND CONTINUING PROFESSIONAL DEVELOPMENT OF DOCTORS · 2019 · DOI

  • The UCLA hard drive theft case and Stanford spreadsheet posting incident illustrate failures in institutional policies for secure storage and transfer of patient data, yet the paper does not identify empirical benchmarks for workforce education programs or technical controls needed to prevent recurrence of similar breaches across multiple healthcare organizations.

    EMERGENTOLOGY: On My Emergency Medicine Boards · 2012 · DOI

  • The paper cites HHS Office of Civil Rights data showing 207 breaches affecting 5.4 million people in 2010 (primarily from human error, theft of laptops/thumb drives, and loss of physical media), but does not address whether existing administrative and civil penalties are calibrated to deter specific breach mechanisms or whether penalty structures differ meaningfully by breach etiology.

    EMERGENTOLOGY: On My Emergency Medicine Boards · 2012 · DOI

  • While the Stanford case demonstrates that third-party vendors receiving encrypted patient information can unilaterally decrypt and repurpose data without institutional oversight, no systematic study exists examining the contractual safeguards, technical controls, or audit mechanisms that effectively prevent unauthorized decryption and disclosure by business associates in healthcare supply chains.

    EMERGENTOLOGY: On My Emergency Medicine Boards · 2012 · DOI

  • The paper identifies that human error is the primary cause of HIPAA privacy breaches (accounting for cases involving decryption of encrypted data, unauthorized spreadsheet creation, and improper file handling), yet provides no empirical framework for quantifying which specific types of human errors (procedural lapses vs. intentional violations) occur most frequently in healthcare vendor-management contexts or how organizational factors influence error rates.

    EMERGENTOLOGY: On My Emergency Medicine Boards · 2012 · DOI

  • These results are inconsistent with Hogarth and Einhorn's (1992) belief-adjustment model, which predicts a recency effect for the step-by-step condition but a primacy effect for the end-of-sequence condition.

    Order of Information Affects Clinical Judgment · 1996 · DOI

  • Obviously the ability of Ss to handle such data remains to be in- vestigated. It remains to be seen whether the development of these methods can lead to the investigation of the extent to which Ss have the ability to make numerical estimates of correlation between binary variables.

    The empirical implications of piaget's concept of correlation · 1972 · DOI

  • Abstract Objectives Anchoring bias occurs when physicians fail to revise an incorrect diagnosis triggered by salient distracting features (SDFs) despite contradictory evidence.

    Cognitive mechanisms underlying anchoring bias in diagnosis: a randomized controlled experiment · 2026 · DOI

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81 open questions have been extracted from the limitations and future-work passages of 976 Clinical Reasoning and Diagnostic Skills 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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