observational study on ai accuracy in diagnosing and treating failed or painful hip arthroplasty

Conditions: Total Hip Arthroplasty (THA)

Summary: primary goal: this study aims to evaluate the diagnostic and therapeutic accuracy of gpt-4 (an advanced ai language model) compared to three orthopedic surgeons with varying experience levels in cases of failed or painful total hip arthroplasty. key research questions: diagnostic accuracy: does gpt-4 provide correct, partially correct, or incorrect diagnoses compared to human orthopaedic surgeons? diagnostic completeness: are gpt-4's diagnostic suggestions complete, partially complete, or incomplete compared to those of orthopedic surgeons? treatment accuracy: does gpt-4 recommend correct, partially correct, or incorrect treatments for failed hip arthroplasty? treatment completeness: are gpt-4's treatment recommendations fully comprehensive, partially complete, or incomplete compared to those of orthopaedic surgeon? study design: participants: 20 anonymized patient cases (ages 18-80) with failed or painful hip arthroplasties, treated at irccs istituto ortopedico rizzoli (bologna, italy) between 2004-2024. cases were selected based on clear diagnostic and treatment records (no ambiguous or incomplete data). comparison groups: gpt-4 (via chatgpt interface) three orthopedic doctors (with different experience levels: resident, specialist, senior surgeon) method: each case (clinical summary + x-ray image) is presented to gpt-4 and the three doctors. they must provide a diagnosis and treatment recommendations. two independent evaluators (principal investigator + department head) blindly assess responses for correctness and completeness using a 3-point scale (0=wrong/incomplete, 2=correct/complete). statistical analysis compares gpt-4 vs. human performance. expected outcomes: determine if ai can match or outperform doctors in diagnosing and treating hip arthroplasty failures. assess whether gpt-4 could serve as a supplementary tool in orthopedic decision-making. ethical \& privacy considerations: no real-time patient data is used-only anonymized past cases. no personal/sensitive data is shared with openai (gpt-4 is used via a standard web interface). study complies with gdpr, hipaa, and ethical ai guidelines. timeline: study duration: \~8 months (from ethics approval to final analysis). results will be published regardless of outcome. why this study matters: first study evaluating gpt-4's role in complex orthopedic diagnostics. could influence future ai-assisted clinical decision-making in joint replacement surgeries.


Eligibility criteria:
inclusion criteria: * adults (≥18 and ≤80 years old). * documented painful or failed total hip arthroplasty requiring clinical/radiological evaluation (2004-2024). * complete pre-operative clinical history, imaging (x-ray/tomography), and surgical reports. * clear diagnosis of failure mode (e.g., aseptic loosening, infection, fracture, wear). * treatment and outcomes fully documented in the institutional database. * "exemplary" cases with minimal diagnostic ambiguity (per engh/musculoskleletal infection society criteria, etc.). exclusion criteria: * total hip arthroplasty with no documented failure/pain (well-functioning implants). * incomplete clinical/radiological records (e.g., missing pre-operative imaging or surgical notes). * complex/multifactorial failures (e.g., concurrent infection + loosening + fracture). * radiographs/images non-interpretable (poor quality, missing views). * cases with conflicting diagnoses/treatments in original records.

Keywords: artificial intelligence total hip arthroplasty diagnosis treatment