Should artificial intelligence augment medical decision making? The case for an autonomy algorithm
Open accessAbstract
. We suggest that the proposed algorithm would result in more accurate predictions than existing methods, which are resource intensive and consider only small patient cohorts. This algorithm could become a valuable tool in medical decision-making processes, augmenting the capacity of all people to make health care decisions in difficult situations.
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- Authors
- Lamanna, C., & Byrne, L.
- Affiliations
- University of Cape Town (ZA)
- The University of Sydney (AU)
- ORCID
- Camillo Lamanna
- Venue
- AMA Journal of Ethics
- Publisher
- American Medical Association (AMA)
- Published
- 2018-09-01
- Volume
- 20
- Issue
- 9
- Pages
- E902-910
- ISSN
- 1937-7010 (ISSN-L)
- 2376-6980
- OpenAlex
- W2889293864
- Cited by
- 78
- References
- 36
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 22 · 2023: 8 · 2022: 8 · 2021: 12 · 2020: 11 · 2019: 2 · 2018: 2
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The AMA Journal of Ethic (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, clinical decision-making, algorithmic decision support, healthcare decision-making, physician autonomy
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Mental Disorders, Decision Making, Forecasting, Health Services for the Aged, Humans, Informed Consent, Technology, Personal Autonomy, Data Mining, Electronic Health Records, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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