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Background: In recent years, there have been numerous studies exploring different teaching methods for improving diagnostic reasoning in undergraduate medical students. This systematic review examines and summarizes the evidence for the effectiveness of these teaching methods during clinical training.
Methods: PubMed, Embase, Scopus, and ERIC were searched. The inclusion criteria for the review consist of articles describing (1) methods to enhance diagnostic reasoning, (2) in a clinical setting (3) on medical students. Articles describing original research using qualitative, quantitative, or mixed study designs and published within the last 10 years (1 April 2009-2019) were included. Results were screened and evaluated for eligibility. Relevant data were then extracted from the studies that met the inclusion criteria.
Results: Sixty-seven full-text articles were first identified. Seventeen articles were included in this review. There were 13 randomized controlled studies and four quasi-experimental studies. Of the randomized controlled studies, six discussed structured reflection, four self-explanation, and three prompts for generating differential diagnoses. Of the remaining four studies, two employed the SNAPPS technique for case presentation. Two other studies explored schema-based instruction and using illness scripts. Twelve out of 17 studies reported improvement in clinical reasoning after the intervention. All studies ranked level two on the New World Kirkpatrick model.
Discussion: The authors posit a framework to teach diagnostic reasoning in the clinical setting. The framework targets specific deficiencies in the students' reasoning process. There remains a lack of studies comparing the effectiveness of different methods. More comparative studies with standardized assessment and evaluation of long-term effectiveness of these methods are recommended.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390726 | PMC |
http://dx.doi.org/10.1007/s11606-021-06916-0 | DOI Listing |
Int J Gen Med
September 2025
Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, USA.
Purpose: The diagnosis of post-acute SARS-CoV-2 infection (PASC) is broad, referring to new or persistent health problems >four weeks after being infected with SARSCoV-2. The aim of this study was to determine whether cytokines, chemokines or catecholamine levels could specify the clinical condition.
Patients And Methods: Seventy-nine participants participated in person to study PASC.
J Eval Clin Pract
September 2025
Department of General Medicine, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Osaka, Japan.
Rationale: Physicians sometimes encounter various types of gut feelings (GFs) during clinical diagnosis. The type of GF addressed in this paper refers to the intuitive sense that the generated hypothesis might be incorrect. An appropriate diagnosis cannot be obtained unless these GFs are articulated and inventive solutions are devised.
View Article and Find Full Text PDFJ Dent
September 2025
Dental Clinic Post-Graduate Program, University Center of State of Pará, Belém, Pará, Brazil. Electronic address:
Objective: This study evaluated the coherence, consistency, and diagnostic accuracy of eight AI-based chatbots in clinical scenarios related to dental implants.
Methods: A double-blind, clinical experimental study was carried out between February and March 2025, to evaluate eight AI-based chatbots using six fictional cases simulating peri-implant mucositis and peri-implantitis. Each chatbot answered five standardized clinical questions across three independent runs per case, generating 720 binary outputs.
Auris Nasus Larynx
September 2025
Objective: To systematically evaluate the diagnostic accuracy, educational utility, and communication potential of generative AI, particularly Large Language Models (LLMs) such as ChatGPT, in otolaryngology.
Data Sources: A comprehensive search of PubMed, Embase, Scopus, Web of Science, and IEEE Xplore identified English-language peer-reviewed studies from January 2022 to March 2025.
Review Methods: Eligible studies evaluated text-based generative AI models used in otolaryngology.
Front Artif Intell
August 2025
Aviation Industry Development Research Center of China, Beijing, China.
Autonomous systems operating in high-dimensional environments increasingly rely on prioritization heuristics to allocate attention and assess risk, yet these mechanisms can introduce cognitive biases such as salience, spatial framing, and temporal familiarity that influence decision-making without altering the input or accessing internal states. This study presents Priority Inversion via Operational Reasoning (PRIOR), a black-box, non-perturbative diagnostic framework that employs structurally biased but semantically neutral scenario cues to probe inference-level vulnerabilities without modifying pixel-level, statistical, or surface semantic properties. Given the limited accessibility of embodied vision-based systems, we evaluate PRIOR using large language models (LLMs) as abstract reasoning proxies to simulate cognitive prioritization in constrained textual surveillance scenarios inspired by Unmanned Aerial Vehicle (UAV) operations.
View Article and Find Full Text PDF