ArticleInternational journal of environmental research and public health2020
A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Practice Makes Human: AI Conversation and Simulation as Deliberate Practice for Human Connection in CPD.Journal of CME · 2026Article
- Assessing the quality of Japanese online breast cancer treatment information using large language models: a comparison of ChatGPT, Claude, and expert evaluations.Breast cancer (Tokyo, Japan) · 2025Article
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- Who is more trustworthy? The influence mechanism of AI vs. human doctor triage on user trust: testing the mediating effect of psychological distance, and a multiple moderating effects analysis.Frontiers in psychology · 2025Article
- Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.JMIR medical education · 2024Article
- Roles considered important for hospitalist and non-hospitalist generalist practice in Japan: a survey study.BMC primary care · 2023Observational
- Clinical Decision Support Systems for Diagnosis in Primary Care: A Scoping Review.International journal of environmental research and public health · 2021Article
- A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery.International journal of environmental research and public health · 2021Article
- Real-World Data and Machine Learning to Predict Cardiac Amyloidosis.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has made great contributions to the healthcare industry. However, its effect on medical diagnosis has not been well explored. Here, we examined a trial comparing the thinking process between a computer and a master in diagnosis at a clinical conference in Japan, with a focus on general diagnosis. Consequently, not only was AI unable to exhibit its thinking process, it also failed to include the final diagnosis. The following issues were highlighted: (1) input information to AI could not be weighted in order of importance for diagnosis; (2) AI could not deal with comorbidities (see Hickam's dictum); (3) AI was unable to consider the timeline of the illness (depending on the tool); (4) AI was unable to consider patient context; (5) AI could not obtain input information by themselves. This comparison of the thinking process uncovered a future perspective on the use of diagnostic support tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.