The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2503.10647 (cs)
[Submitted on 2 Mar 2025 (v1), last revised 29 Jul 2026 (this version, v2)]
Title:The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness
Authors:Krishna Subedi
View a PDF of the paper titled The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness, by Krishna Subedi
View PDF
HTML (experimental)
Abstract:This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dimensions: consistency under rephrased inputs, susceptibility to irrelevant prompt content, and responsiveness to added clinical context. We designed 52 clinical scenarios and modified each under controlled conditions. For consistency, scenarios were rephrased with demographic, wording, and examination changes that preserved the diagnostic core. And the susceptibility was evaluated through embedding irrelevant but plausible narrative details while keeping the clinical evidence unchanged. For contextual awareness, patient history, lifestyle data, or diagnostic findings were added to shift the expected diagnosis. Physician reviewers then judged whether context-driven changes were clinically appropriate. Both models returned identical diagnoses across all equivalent variants and repeated queries (100% consistency). When irrelevant details were added, Gemini changed its diagnosis in 40.0% of cases and ChatGPT in 30.0%. ChatGPT responded to context more often than Gemini (77.8% vs. 55.6%), but a larger share of its changes were clinically inappropriate (33.3% vs. 22.2%). Gemini's context-driven changes were more often judged appropriate (66.7% vs. 55.6%). Consistency under controlled inputs did not protect either model from irrelevant manipulation or unjustified diagnostic shifts when context changed. Before LLMs can separate relevant from irrelevant input and flag insufficient evidence, their diagnostic use requires clinician oversight and structured safeguards.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2503.10647 [cs.CL] |
| (or arXiv:2503.10647v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2503.10647
arXiv-issued DOI via DataCite
|
Submission history
From: Krishna Subedi [view email][v1] Sun, 2 Mar 2025 11:50:16 UTC (526 KB)
[v2] Wed, 29 Jul 2026 08:10:38 UTC (496 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
View a PDF of the paper titled The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness, by Krishna Subedi
Current browse context:
cs.CL
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Geometric and Behavioral Stratification in Transformer Residual Streams
Aug 14
-
Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia
Aug 14
-
I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization
Aug 14
-
Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"
Aug 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.