Clinical trial NCT07012577: Observational Study on AI Accuracy in Diagnosing and Treating Failed or Painful Hip Arthroplasty
Official title: Observational Study on the Accuracy and Completeness of General Artificial Intelligence in the Diagnosis and Therapeutic Recommendations for Failed or Painful Total Hip Arthroplasty
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Total Hip Arthroplasty (THA)
Keywords: Artificial intelligence
total hip arthroplasty
diagnosis
treatment
Lead sponsor: Istituto Ortopedico Rizzoli
Sponsor class: OTHER
First posted: 2025-06-10
Enrollment: 20
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This study evaluated 20 anonymized cases of failed/painful total hip arthroplasty (2004-2024) from a tertiary center. Cases were selected for diagnostic clarity (e.g., aseptic loosening, periprosthetic fracture) and excluded if records were incomplete or ambiguous. Two senior surgeons verified case eligibility. Each case was assessed by GPT-4, an arthroplasty fellow, a 4th-year resident, and a 3rd-year resident for diagnostic/therapeutic accuracy.

Brief 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.

Primary outcomes:
measure: Diagnostic correctness
timeFrame: Immediate (post-case evaluation)
measure: Diagnostic completeness
timeFrame: Immediate (post-case evaluation)
measure: Treatment recommendation correctness
timeFrame: Immediate (post-case evaluation)
measure: Treatmetn recommendation completeness
timeFrame: Immediate (post-case evaluation)
Secondary outcomes:
Other outcomes:
Locations:
city: Bologna
country: Italy
facility: SC Ortopedia e Traumatologia e Chirurgia Protesica e dei Reimpianti di Anca e Ginocchio, IRCCS Istituto Ortopedico Rizzoli
Study officials:
affiliation: IRCCS Istituto Ortopedico Rizzoli
name: Francesco Castagnini, MD
role: PRINCIPAL_INVESTIGATOR
References:
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citation: Dagher T, Dwyer EP, Baker HP, Kalidoss S, Strelzow JA. "Dr. AI Will See You Now": How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines? Clin Orthop Relat Res. 2024 Dec 1;482(12):2098-2106. doi: 10.1097/CORR.0000000000003234. Epub 2024 Sep 6.
citation: Artioli E, Veronesi F, Mazzotti A, Brogini S, Zielli SO, Giavaresi G, Faldini C. Assessing ChatGPT responses to common patient questions regarding total ankle arthroplasty. J Exp Orthop. 2024 Dec 31;12(1):e70138. doi: 10.1002/jeo2.70138. eCollection 2025 Jan.
citation: Pagano S, Strumolo L, Michalk K, Schiegl J, Pulido LC, Reinhard J, Maderbacher G, Renkawitz T, Schuster M. Evaluating ChatGPT, Gemini and other Large Language Models (LLMs) in orthopaedic diagnostics: A prospective clinical study. Comput Struct Biotechnol J. 2024 Dec 26;28:9-15. doi: 10.1016/j.csbj.2024.12.013. eCollection 2025.