A Call to Address AI “Hallucinations” and How Healthcare Professionals Can Mitigate Their Risks
Open accessAbstract
Artificial intelligence (AI) has transformed society in many ways. AI in medicine has the potential to improve medical care and reduce healthcare professional burnout but we must be cautious of a phenomenon termed "AI hallucinations"and how this term can lead to the stigmatization of AI systems and persons who experience hallucinations. We believe the term "AI misinformation" to be more appropriate and avoids contributing to stigmatization. Healthcare professionals can play an important role in AI's integration into medicine, especially regarding mental health services, so it is important that we continue to critically evaluate AI systems as they emerge.
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The quality, scope, and currency of data accessible to LLMs are relevant to their clinical performance.
“The quality, scope, and currency of data accessible to LLMs are relevant to their clinical performance.86,101,106”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [101]
The Editorial (page not specified) says LLMs are only as accurate as the information provided to them and calls for more diverse, representative datasets, supporting the relevance of data quality and scope to clinical use. It calls for continuous improvement of available data but does not specifically address whether the currency of that data affects clinical performance. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAdvanced AI systems have the ability to produce hallucinations-fictitious or fabricated output presented as fact.
“Advanced AI systems have the ability to produce “hallucinations” – fictious or fabricated output which is presented as fact [13].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [13]
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Self-citationChatbot Confabulations Are Not Hallucinations—Reply
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Kim, Y. I., Kim, K. H., Oh, H.-J., Seo, Y., Kwon, S. M., Sung, K. S., Chong, K., & Lee, M. H. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.05.023 · Cited by 3 · see citing works
Rare diseases: ethical challenges in the era of digital health
Siderius, L., Perera, S. D., Jankauskaitė, L., Bhattacharya, A., & Gonçalves, P. J. S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1539841 · Cited by 3 · see citing works
Challenging the dogma: why reviewers should be allowed to use AI tools
Lippi, G., Lackner, K. J., Melichar, B., Pan, S., Schlattmann, P., Greaves, R. F., Gillery, P., & Plebani, M. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2025-1180 · Cited by 3 · see citing works
AI scribes in rural and remote primary care: an antidote to physician burnout or Pandora’s Box?
Bressan, Bakke, Zelek, Cotterill, & Wood (2025) · Rural and Remote Health · doi:10.22605/rrh9430 · Cited by 2 · see citing works
Artificial Intelligence in Perioperative Medication-Related Clinical Decision Support
Patel, M., & Nanji, K. C. (2025) · Anesthesiology Clinics · doi:10.1016/j.anclin.2025.05.009 · Cited by 2 · see citing works
In Good Faith: Generative AI’s Plausible Falsehoods and Their Implications for Trust
Kodish, S. (2025) · Atlantic Journal of Communication · doi:10.1080/15456870.2025.2533224 · Cited by 2 · see citing works
Collins, C., Fackler, J. C., Sacco, M. J., & Jacobs, M. L. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1646989 · Cited by 3 · see citing works
- Authors
- Hatem, R., Simmons, B., & Thornton, J. E.
- Affiliations
- University of Florida Health (US)
- Corresponding author
- Thornton, Joseph E
- ORCID
- Thornton, Joseph E
- Venue
- Cureus
- Published
- 2023-09-05
- Volume
- 15
- Issue
- 9
- Pages
- e44720-e44720
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10552880
- OpenAlex
- W4386459994
- Cited by
- 175
- References
- 4
- Citations by year
- 2026: 49 · 2025: 86 · 2024: 39 · 2020: 1
- 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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI hallucination, AI misinformation, artificial intelligence in healthcare, healthcare workers, mental health services, risk mitigation, burnout
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.
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