{
  "abstract": "Objective: Large language models (LLMs) have demonstrated promise in healthcare for clinical documentation and decision support. Much of the information in electronic medical records, however, is structured or clinician-synthesized documentation. There is less certainty about the capability of LLMs in devising clinically appropriate diagnostic hypotheses from unstructured data such as a patient interview. Method: Deidentified clinical interview transcripts from 32 older adults referred for neuropsychological evaluation of memory complaints were submitted to 10 commercially available LLMs using an institutionally approved platform with a standardized prompt. Models classified each case using four categories: normal, mild cognitive impairment (MCI), dementia, or uncertain. Model classifications were compared with clinician reference diagnoses based on neuropsychological evaluation. Results: Model-inferred and reference diagnoses showed exact agreement rates from 18.8% to 56.2% with poor to slight chance-corrected agreement (Cohen’s κ = −.178 to .196). Classification errors across models were more often overclassification rather than underclassification. Using dichotomous cognitive status (i.e., normal versus impaired), seven of ten models showed sensitivity over 90% for identifying impairment, but specificity ranged from 0 to 60%. Workflow response time varied by model and was not associated with accuracy. Conclusions: Using unstructured and unannotated clinical interview data, general-purpose LLMs showed limited, model-dependent agreement with clinician diagnoses and a tendency to overclassify impairment. LLMs warrant further evaluation as tools that support clinical workflows, with revised prompting strategies and systematic efforts to improve classification.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Anthony Hachem"
    },
    {
      "name": "Chloe Chui"
    },
    {
      "name": "Elliot T. Morgan"
    },
    {
      "name": "Ashika S. Bakre"
    },
    {
      "name": "Summer Rolin"
    },
    {
      "name": "Anthony Rios"
    },
    {
      "name": "Jeremy Davis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5da9rk8jg6zmg.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/6pmca_v1",
  "fulltext_url": null,
  "id": "5da9rk8jg6zmg",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/6pmca_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Clinical Decision Making"
    ],
    [
      "bepress",
      "Clinical Neuropsychology"
    ],
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220659096",
  "pdf_url": "https://osf.io/6pmca/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5da9rk8jg6zmg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:38Z",
      "native_id": "6pmca",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/6pmca_v1/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Evaluating Large Language Model Diagnostic Classification in Neuropsychology",
  "title_text": "Evaluating Large Language Model Diagnostic Classification in Neuropsychology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:38Z",
  "url": "https://research.draconitic.com/paper/5da9rk8jg6zmg",
  "venue": "PsyArXiv",
  "versions": []
}