Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making
Open accessAbstract
This case report reflects on a delayed diagnosis for a 27-year-old woman who reported chest pain and shortness of breath to the emergency department. The treating clinician reflects upon how cognitive biases influenced their diagnostic process and how multiple missed opportunities resulted in missteps. Using artificial intelligence (AI) tools for clinical decision-making, we suggest how AI could augment the clinician, and in this case, delayed diagnosis avoided. Incorporating AI tools into clinical decision-making brings potential benefits, including improved diagnostic accuracy and addressing human factors contributing to medical errors. For example, they may support a real-time interpretation of medical imaging and assist clinicians in generating a differential diagnosis in ensuring that critical diagnoses are considered. However, it is vital to be aware of the potential pitfalls associated with the use of AI, such as automation bias, input data quality issues, limited clinician training in interpreting AI methods, and the legal and ethical considerations associated with their use. The report draws attention to the utility of AI clinical decision-support tools in overcoming human cognitive biases. It also emphasizes the importance of clinicians developing skills needed to steward the adoption of AI tools in healthcare and serve as patient advocates, ensuring safe and effective use of health data.
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Recent research suggests that AI tools showing human-level performance on language-based tasks may help close the novice-to-expert gap.
“Recent research suggests that artificial intelligence tools showing human‐level performance on language‐based tasks may help close this gap (Alshehri et al., 2023; Brown et al., 2023).”
From A comparative vignette study: Evaluating the potential role of a generative <scp>AI</scp> model in enhancing clinical <scp>decision‐making</scp> in nursing (2025) · cites this paper as Brown et al., 2023
The Discussion says some AI tools rival or exceed expert clinicians’ diagnostic accuracy and that natural-language processing can help clinicians generate differential diagnoses. It does not say that AI achieves human-level performance on language-based tasks or show that it closes a novice-to-expert gap. The relevant passages in our copy have no page numbers. (LLM, conf=0.84)
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Cites
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Cited by
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AI in diagnostic imaging: Revolutionising accuracy and efficiency
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“Recent research suggests that artificial intelligence tools showing human-level performance on language-based tasks may help close this gap (Alshehri et al., 2023 ; Brown et al., 2023 ).”
- Authors
- Brown, C., Nazeer, R., Gibbs, A., Le Page, P., & Mitchell, A. R.
- Affiliations
- Government of Jersey (JE)
- Corresponding author
- Brown, Chris
- Venue
- Cureus
- Published
- 2023-03-20
- Volume
- 15
- Issue
- 3
- Pages
- e36415-e36415
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10115193
- OpenAlex
- W4327934511
- Cited by
- 55
- References
- 3
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 27 · 2023: 8
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, clinical decision support, cognitive biases, delayed diagnosis, emergency department, chest pain, medical errors, automation bias, AI adoption in healthcare, medical ethics, data quality, clinician training
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