Pre-Visit AI Symptom-Checking and Shared Decision-Making in Spine Physical Therapy

Recruiting now

Conditions studied: Low Back Pain, Neck Pain, Radiculopathy

In brief

Artificial intelligence (AI) symptom-checking tools, including large language models such as ChatGPT, are increasingly used by patients before they seek care. These tools may shape patients' beliefs about their diagnosis, how serious they think their condition is, and when they decide to seek treatment. It is not yet known how this pre-visit AI use affects the initial physical therapy encounter for spine-related problems. This prospective observational cohort study examines whether prior use of AI symptom-checking tools influences the first physical therapy evaluation in adults presenting with spine-related musculoskeletal complaints (neck, thoracic, or low back pain, with or without radicular symptoms). Consecutive patients attending an outpatient physical therapy clinic for a new evaluation are grouped as AI users or non-AI users based on whether they used such a tool for their current complaint in the previous 30 days. The primary outcome is shared decision-making, measured with the SDM-Q-9 immediately after the initial evaluation. Secondary outcomes include stage of presentation, agreement between the patient's expected diagnosis and the clinician's classification, baseline pain and disability, functional performance, and clinical outcomes at 2 and 6 weeks. The investigators hypothesize that prior AI use is associated with differences in shared decision-making and in how patients present for care.

Key facts

Study ID
NCT07733752
Run by
Assiut University
People needed
200
Starts
2026-06-10
Expected to finish
2027-02-10
Last updated by the study team
2026-07-29

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

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You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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