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<p>Motor complications become increasingly prominent as Parkinson’s disease (PD) progresses. Although device-aided therapies (DAT) improve symptoms control in advanced PD, referral practices remain inconsistent, partly due to unequal access to specialist centers. Screening tools such as MANAGE-PD help identify patients who may benefit from DAT, while large language models (LLMs) are emerging as scalable clinical decision-support tools. To assess the screening performance and clinical safety of LLM-based triage in identifying PD patients eligible for DAT and to compare LLMs classifications with specialist assessments and MANAGE-PD. This retrospective single-center study included 279 DAT-naïve PD patients referred for advanced therapy evaluation. Clinical data covering motor complications, treatment burden, functional impairment and non-motor symptoms were transformed into standardized narrative case summaries. Seven commercially available LLMs were evaluated in a zero-shot setting, and model outputs were compared with specialist qualification decisions used as the reference standard, and with MANAGE-PD classifications. Sensitivity, specificity, predictive values, accuracy and agreement statistics were analyzed. The best-performing model (gemini-2.5-flash) achieved 100% sensitivity with no false-negative classifications. Specificity reached 69.2%, and overall accuracy was 78.5%. Agreement with specialist decisions was similar for LLM-based screening and MANAGE-PD (κ ≈ 0.575 vs. 0.567), with no significant differences in Cohen’s kappa (<i>p</i> = 0.81) or discordant classifications (McNemar <i>p</i> = 1.0). LLM-based triage achieved high-sensitivity screening performance comparable to an established clinical screening tool. These findings support a potential role for LLMs as scalable support systems within DAT referral pathways, complementing rather than replacing specialist clinical assessment.</p>

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Large language models as a screening tool for the qualification of patients with Parkinson’s disease for device-aided therapies

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Paweł Pobudejski,
  • Mateusz Toś,
  • Krzysztof Nyczka,
  • Krzysztof Duda,
  • Tomasz Chmiela,
  • Joanna Siuda

摘要

Motor complications become increasingly prominent as Parkinson’s disease (PD) progresses. Although device-aided therapies (DAT) improve symptoms control in advanced PD, referral practices remain inconsistent, partly due to unequal access to specialist centers. Screening tools such as MANAGE-PD help identify patients who may benefit from DAT, while large language models (LLMs) are emerging as scalable clinical decision-support tools. To assess the screening performance and clinical safety of LLM-based triage in identifying PD patients eligible for DAT and to compare LLMs classifications with specialist assessments and MANAGE-PD. This retrospective single-center study included 279 DAT-naïve PD patients referred for advanced therapy evaluation. Clinical data covering motor complications, treatment burden, functional impairment and non-motor symptoms were transformed into standardized narrative case summaries. Seven commercially available LLMs were evaluated in a zero-shot setting, and model outputs were compared with specialist qualification decisions used as the reference standard, and with MANAGE-PD classifications. Sensitivity, specificity, predictive values, accuracy and agreement statistics were analyzed. The best-performing model (gemini-2.5-flash) achieved 100% sensitivity with no false-negative classifications. Specificity reached 69.2%, and overall accuracy was 78.5%. Agreement with specialist decisions was similar for LLM-based screening and MANAGE-PD (κ ≈ 0.575 vs. 0.567), with no significant differences in Cohen’s kappa (p = 0.81) or discordant classifications (McNemar p = 1.0). LLM-based triage achieved high-sensitivity screening performance comparable to an established clinical screening tool. These findings support a potential role for LLMs as scalable support systems within DAT referral pathways, complementing rather than replacing specialist clinical assessment.