The Problem: Progress in teaching and learning clinical reasoning depends upon more sophisticated modelling of the reasoning process itself. Current accounts of clinical reasoning, grounded in experimental psychology, show a bias towards situating reasoning inside the skull, further reduced to neural processes signified by imaging. Such a model is necessary but not sufficient to explain the clinical reasoning process where it fails to embrace cognition extended to the environment and social contexts.
A Solution: Sufficiency for a model of clinical reasoning must include dialogues between doctor, patient, and colleagues, including the complex influences of history and culture, where artefacts and semiotics such as computers, testing, and narrative structures augment cognition. Here, 'extended' cognition is configured as an outside-in process of 'sensemaking' or 'adaptive expertise'.
The Future: Current 'predictive processing' cognition models place emphasis on anticipatory cognition, where memory is reconfigured as active reconstruction rather than recall and recognition. Such an 'ecological perception' or 'externalistic' model provides a counter to the current dominant paradigm of 'ego-logical' cognitive reasoning - the latter, again, abstracted from context and located inside the skull. New models of clinical reasoning as an open, dynamic, nonlinear, complex system are called for.
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http://dx.doi.org/10.1080/0142159X.2020.1859098 | DOI Listing |
J Interprof Care
January 2025
University of South Australia Allied Health and Human Performance, South Australia, Australia.
Allied health clinical educators (AHCEs) are vital to health professional student education and clinical education is often expected in a job role. Communities of practice (CoPs) may be a strategy to meet educator learning needs. A rapid review was conducted to determine the structures, purposes, and outcomes of AHCE CoPs, and barriers or enablers of participation in CoPs.
View Article and Find Full Text PDFJ Med Educ Curric Dev
January 2025
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
Large group collaborative teaching approaches are rapidly gaining popularity in undergraduate medical education. The case-based collaborative Learning (CBCL) pedagogy was instituted for pre-clerkship teaching at Harvard Medical School in 2015 with subsequent implementation at other medical schools. CBCL emphasizes inductive reasoning, integrates basic and clinical sciences, stimulates curiosity, and fosters teamwork.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Research Center for Noncommunicable Disease, Jahrom University of Medical Sciences, Jahrom, Iran.
Background: Breast cancer (BC) is a global challenge that affects a large portion of individuals, especially women. It has been suggested that microparticles (MPs) can be used as a diagnostic, prognostic, or therapeutic biomarker in various diseases. Moreover, MPs are known to elevate in cancer cases.
View Article and Find Full Text PDFRadiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
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