ArticleNeurospine2024
Use of ChatGPT for Determining Clinical and Surgical Treatment of Lumbar Disc Herniation With Radiculopathy: A North American Spine Society Guideline Comparison.
Article in Neurospine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Examining the Role of Large Language Models in Orthopedics: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Evaluating reasoning-tuned large language models for clinical decision-making in spine surgery.Spine deformity · 2026Article
- Large language models in the vertical integration of spine surgery workflow: a scoping review.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Large language models in spine care and research.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Performance of DeepSeek-R1 and ChatGPT-5 in the Generation of North American Spine Society Clinical Guidelines for Adult Vertebral Compression Fractures: Comparative Study.Journal of medical Internet research · 2026Article
- A comparative evaluation of large language models in aligning with European Respiratory Society (ERS) guidelines for high-flow nasal cannula in acute respiratory failure.Journal of thoracic disease · 2026Article
- Article
- The Use of Chat-GPT 5.2 by Patients Affected by Rotator Cuff Tears Leads to Inaccurate Diagnosis and Treatment Suggestions: A Study by SICSeG (Italian Society of Shoulder and Elbow Surgery).Journal of clinical medicine · 2026Article
- Use of Generative Artificial Intelligence in the Management of Low Back Pain: a Scoping Review.Journal of medical systems · 2026Article
- A systematic comparison of ChatGPT and DeepSeek for guideline-based question answering in obstetric anesthesia.Scientific reports · 2026Article
- Evaluation of retrieval-augmented generation and large language models in clinical guidelines for degenerative spine conditions.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.Frontiers in endocrinology · 2026Article
- Multiple large language models versus clinical guidelines for postmenopausal osteoporosis: a comparative study of ChatGPT-3.5, ChatGPT-4.0, ChatGPT-4o, Google Gemini, Google Gemini Advanced, and Microsoft Copilot.Archives of osteoporosis · 2025Article
- Current trends and future prospects of language models and processing systems in spine surgery - a scoping review.Neurosurgical review · 2025Article
- Systematic Review on Large Language Models in Orthopaedic Surgery.Journal of clinical medicine · 2025Review
- Evaluation of the performance of large language models in endoscopic lumbar surgery: a comparative analysis.Annals of medicine and surgery (2012) · 2025Article
- Large Language Models in Spine Surgery: A Promising Technology.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2025Review
- The assessment of ChatGPT-4's performance compared to expert's consensus on chronic lateral ankle instability.Journal of experimental orthopaedics · 2025Article
- Evaluating ChatGPT and DeepSeek in postdural puncture headache management: a comparative study with international consensus guidelines.BMC neurology · 2025Article
Corrections and comments
- Commented on by
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveLarge language models like chat generative pre-trained transformer (ChatGPT) have found success in various sectors, but their application in the medical field remains limited. This study aimed to assess the feasibility of using ChatGPT to provide accurate medical information to patients, specifically evaluating how well ChatGPT versions 3.5 and 4 aligned with the 2012 North American Spine Society (NASS) guidelines for lumbar disk herniation with radiculopathy.
methodsChatGPT's responses to questions based on the NASS guidelines were analyzed for accuracy. Three new categories-overconclusiveness, supplementary information, and incompleteness-were introduced to deepen the analysis. Overconclusiveness referred to recommendations not mentioned in the NASS guidelines, supplementary information denoted additional relevant details, and incompleteness indicated omitted crucial information from the NASS guidelines.
resultsOut of 29 clinical guidelines evaluated, ChatGPT-3.5 demonstrated accuracy in 15 responses (52%), while ChatGPT-4 achieved accuracy in 17 responses (59%). ChatGPT-3.5 was overconclusive in 14 responses (48%), while ChatGPT-4 exhibited overconclusiveness in 13 responses (45%). Additionally, ChatGPT-3.5 provided supplementary information in 24 responses (83%), and ChatGPT-4 provided supplemental information in 27 responses (93%). In terms of incompleteness, ChatGPT-3.5 displayed this in 11 responses (38%), while ChatGPT-4 showed incompleteness in 8 responses (23%).
conclusionChatGPT shows promise for clinical decision-making, but both patients and healthcare providers should exercise caution to ensure safety and quality of care. While these results are encouraging, further research is necessary to validate the use of large language models in clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.