The Integration of Large Language Models in Diagnostic Pattern Recognition
Evaluating the role of language models as adjunctive tools in primary care and metabolic screening.
Large language models increasingly appear in workflows that were historically the domain of narrow clinical decision-support systems. Their strength is not in producing diagnoses but in surfacing patterns across dispersed data.
In our practice, we have found the most utility in structured intake summarization, differential broadening, and preparation for consultations — never as a substitute for clinical judgment.
The limitations are well-known: hallucination, sensitivity to prompt wording, and inconsistent citation of sources. Clinical use therefore requires explicit boundaries and validation.
This review outlines a pragmatic framework: identify tasks where the tool's failure modes are acceptable, define the human-in-the-loop clearly, and monitor outputs against a reference.
Key findings
- Effect sizes vary substantially across studies.
- Applicability to healthy adults remains contingent on further evidence.
- Careful monitoring is warranted when interventions are used off-label.
Clinical context
Recommendations should always be considered within the individual patient's clinical picture.
Limitations
Publication bias, heterogeneous protocols, and short follow-up limit strong conclusions.
References
- Author et al. Journal Placeholder. Year. Volume, Pages.
- Author et al. Journal Placeholder. Year. Volume, Pages.
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