The DOI matches its Crossref record; its reference list is not available.
AI in Medicine
Abstract
This chapter explores the use of artificial intelligence in medicine. AI promises major benefits for healthcare. But along with the benefits come risks. Not so much the risk of powerful super-intelligent machines taking over, but the risk of structural injustices, biases, and inequalities being perpetuated in a system that cannot be challenged because nobody actually knows how the algorithms work. Or the risk that there might be no doctor or nurse present to hold your hand and reassure you when you are at your most vulnerable. There are many initiatives to come up with ethical or trustworthy AI and these efforts are important. Yet we should demand more than this. Technological solutionism and the urge to “move fast and break things” often dominate the tech industry but are inappropriate for the healthcare context and incompatible with basic healthcare values of empathy, solidarity, and trust.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are concerns about the deployment of insufficiently validated clinical models.
“This raises concerns about the deployment of insufficiently validated clinical models. [12]”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [12]
Citations
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
Cited by 3
Metadata
- Authors
- Kerasidou, A., & Kerasidou, X. (.
- ORCID
- Kerasidou, Angeliki
- Venue
- Future Morality
- Publisher
- Oxford University Press
- Published
- 2021-09-23
- Pages
- 83-92
- OpenAlex
- W3207872699
- Cited by
- 3
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1
- FWCI
- 0.71
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in healthcare, algorithmic bias, structural injustice, health care disparities, algorithmic transparency, ethical AI, trustworthy AI, technological solutionism, health care value, empathy in healthcare, trust in healthcare, human-AI collaboration
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 ›
Are you an author, or do you see a problem with this page? Request a review
