Approaching autonomy in medical artificial intelligence
Open accessAbstract
Artificial intelligence (AI) has the potential to transform the delivery of health care by automating complex tasks that traditionally required substantial training and expertise. Contrary to concerns that fully automated AI will replace health-care providers,1 a more likely scenario is that providers will increasingly interact with task-specific and domain-specific AI systems across a continuum of automation. The level of AI autonomy is broadly accepted to be crucial to the risk-assessment of AI algorithms, yet its definition and classification is poorly defined.
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In medical imaging, automation bias can manifest when clinicians or radiologists place undue trust in AI outputs or recommendations, leading them to overlook important information or make errors in diagnosis or treatment planning.
“In the context of AI in medical imaging, automation bias can manifest when clinicians or radiologists place undue trust in the outputs or recommendations provided by AI algorithms, leading them to overlook potentially important information or make errors in diagnosis or treatment planning.43”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [43]
The incorporation of AI into health care decision-making can affect the presentation and comprehension of options by patients.
“The incorporation of AI into health care decision-making can affect the presentation and comprehension of options by patients.”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [60]
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- Authors
- Bitterman, D. S., Aerts, H. J. W. L., & Mak, R. H.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Mak, Raymond H
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 2
- Issue
- 9
- Pages
- e447-e449
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W3080484018
- Cited by
- 107
- References
- 3
- Citations by year
- 2026: 24 · 2025: 21 · 2024: 21 · 2023: 17 · 2022: 13 · 2021: 11
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in healthcare, AI risk assessment, clinical decision support, AI classification, healthcare automation
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans
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