ArticleJournal of medical Internet research2021
How Clinicians Perceive Artificial Intelligence-Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 5 of them syntheses that pooled it.
What it found
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Who cites it
30 citing papers in PubMed, 5 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled it
- Pooled it
- Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students' perspectives: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.European stroke journal · 2026Article
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- The role of artificial intelligence in advancing urologic care: From diagnostics to therapeutics.Surgery in practice and science · 2026Review
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- Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health.Veterinary sciences · 2026Review
- Designing a Digital Twin for the Management of Noncommunicable Diseases: Protocol for a Pilot Study and Methodology Validation.JMIR research protocols · 2026Article
- Online survey assessing US primary care physicians' attitudes toward AI use in clinical administrative tasks.BMJ health & care informatics · 2026Article
- Patient and dental practitioner acceptance of artificial intelligence in dental care: a cross-sectional study in Saudi Arabia's eastern province.Frontiers in oral health · 2026Article
- LLM-Powered Cross-Modal Alignment for Explainable Seizure Detection from EEG.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.Journal of medical Internet research · 2025Article
- A Model Predicting Artificial Intelligence Use by Gastroenterology Nurses in Clinical Practice: A Cross-Sectional Multicenter Survey.Journal of gastroenterology and hepatology · 2025Article
- Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs).BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Primary Care: Support or Additional Burden on Physicians' Healthcare Work?-A Qualitative Study.Clinics and practice · 2025Article
- Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis.International journal of nursing knowledge · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making.
objectiveThis study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care.
methodsThis study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques.
resultsA total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (β=-0.421, P=.02). Instead, clinicians' diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms.
conclusionsThis study elucidated clinicians' current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis.
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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.