ArticleJournal of pain research2026
Smart Processing and Intelligent Navigation for Evaluation (SPINE): Comparing Clinicians and AI Language Model (GPT-4) in Spinal Cord Stimulation Candidate Selection.
Article in Journal of pain research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Large Language Models in Spine Surgery: A Scoping Review of Clinical Efficacy, Technical Integration, and Ethical Paradigms.Global spine journal · 2026Review
- Leveraging artificial intelligence to optimize neuromodulation in the treatment of patients with chronic pain.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Explainable artificial intelligence for postoperative analgesia risk stratification and clinical decision support in abdominal surgery.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the continued advancement of artificial intelligence (AI), large language models (LLMs) such as GPT-4 may assist clinicians in evaluating patient candidacy for spinal cord stimulation (SCS). We compared a general-purpose, non-fine-tuned LLM (GPT-4), an expert multidisciplinary team (MDT), and a clinician-input, rule-based e-Health decision-support tool. The study focused exclusively on decision agreement and did not assess clinical outcomes (eg, pain relief or device retention). Methods: This single-center, retrospective cohort was conducted at Fondazione Istituto G. Giglio (Cefalù, Italy) and included 93 consecutive adults referred to the MDT for SCS evaluation between January 2022 and March 2024. The MDT issued binary recommendations ("proceed" vs "do not proceed") as the reference standard. The e-Health tool generated "yes", "maybe", or "no" outputs from structured clinician-entered data. GPT-4 was applied zero-shot, using a single standardized prompt on anonymized vignettes within an offline environment. The primary endpoint was agreement (weighted κ) among MDT, e-Health, and GPT-4; sensitivity/specificity analyses explored three interpretations of "maybe". Results: The MDT recommended SCS for 91.4% of patients, compared with 54.8% for the e-Health tool and 46.2% for GPT-4. Agreement was moderate for MDT vs e-Health (κ = 0.51) and e-Health vs GPT-4 (κ = 0.46), and fair for MDT vs GPT-4 (κ = 0.29). GPT-4 demonstrated a more conservative profile, favoring specificity over sensitivity. Conclusion: A non-fine-tuned GPT-4 approximated but did not replicate MDT decision-making, functioning as a high-specificity, low-sensitivity filter. A layered workflow combining rule-based tools with expert oversight and targeted LLM adaptation may best optimize SCS candidate selection.
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