Generative AI and Large Language Models in Orthopaedics

While Computer Vision handles the X-rays, Generative AI handles the words. Large Language Models (LLMs) like GPT-4, Claude, and Med-PaLM have exploded onto the scene, offering a level of text understanding and generation that was science fiction just a few years ago.

For the orthopaedic surgeon, who spends nearly 50% of their time on documentation and administrative tasks, this technology offers the promise of liberation. But it also brings risks of hallucination and data privacy that must be managed.

What is Generative AI?

Unlike "Discriminative AI" (which classifies data: "Fracture" vs "No Fracture"), Generative AI creates new data. It predicts the next word in a sentence based on probability, trained on the sum total of the internet's text.

When fine-tuned on medical literature (like Google's Med-PaLM 2), these models pass the USMLE with flying colors and can reason through complex clinical vignettes. They are not just search engines; they are reasoning engines.

Clinical Applications of LLMs

1. The Automated Scribe (Ambient Clinical Intelligence)

This is the "Killer App" for burnout.

• The Workflow: The surgeon walks into the clinic room. A secure app on their phone (e.g., Nuance DAX, Nabla) listens to the conversation with the patient.
• The Magic: The AI filters out the small talk ("How's the golf game?"), extracts the relevant HPI, Exam findings (dictated by the surgeon), and Plan.
• The Output: It instantly generates a perfectly formatted clinic letter: "Mr. Smith presents with 3 months of medial knee pain...".
• The Benefit: Surgeons save 2-3 hours of typing per day. Eye contact is restored with the patient instead of staring at a screen.

2. Patient Communication and Education

"Doctor, what does 'meniscal extrusion' mean?"

• Translation: LLMs are incredible translators—not just between languages, but between "Medicalese" and "Plain English."
• Custom Instructions: You can prompt an AI: "Rewrite this operative report as a letter to the patient explaining what we did, at a 6th-grade reading level."
• Chatbots: Hospitals are deploying AI chatbots to triage post-op questions. "Is my wound drainage normal?" The AI can assess the description/photo and advise "Normal" or "Go to ED."

3. The "Silicon Resident" (Decision Support)

While not yet FDA-approved for autonomous diagnosis, LLMs act as a powerful sounding board.

• Complex Cases: "I have a 45-year-old male with a history of renal transplant, presenting with hip pain and normal X-rays. What is the differential?" The AI will instantly suggest "AVN, transient osteoporosis, stress fracture," and suggest the next best test (MRI).
• Guideline Adherence: "What is the current AAOS guideline for VTE prophylaxis in total ankle replacement?" The AI retrieves the specific protocol instantly.

Research and Education

Data Extraction

Doing a systematic review?

• Old Way: Manually reading 500 abstracts to see if they fit criteria.
• New Way: Feed the 500 abstracts into an LLM with the prompt: "Extract the sample size, follow-up time, and infection rate from each of these summaries."

Writing Assistance

LLMs can draft introductions, format references, and check grammar. (Note: They should never write the discussion or results, as they can hallucinate).

Future Trends: Autonomous Robotics

Where do LLMs meet the real world? Embodied AI. Current robots (Mako, ROSA) are "Cobots"—collaborative robots that require the surgeon's hand. The next generation will be autonomous.

• The Concept: An AI model that learns from video. By watching 10,000 hours of knee replacements, the robot learns the subtle movements of soft tissue handling.
• The Future: A robot that can suture, dissect, and close autonomously under surgeon supervision.

The Risks: Hallucination and Privacy

1. Hallucinations

LLMs are designed to be plausible, not truthful. They can confidently invent citations that do not exist or guidelines that are incorrect.

Rule #1: Never trust an LLM without verifying the source. It is a confident liar.

2. Data Privacy (HIPAA/GDPR)

Public models (like free ChatGPT) train on the data you feed them.

Rule #2: Never paste Patient Health Information (PHI) like names or MRNs into a public chatbot. Use enterprise-grade, secure "walled garden" instances.

Prompt Engineering for Surgeons

Getting good results requires good prompts. Use the R-C-O framework:

• R (Role): "Act as an expert orthopaedic hip surgeon."
• C (Context): "I am writing a letter to a GP about a patient with OA. The patient is hesitant about surgery."
• O (Output): "Produce a concise, professional letter. Do not use jargon. Focus on the non-operative options we discussed."

Conclusion

Generative AI is the "bicycle for the mind." It amplifies the surgeon's ability to communicate, document, and reason. Those who embrace it will find themselves with more time for the operating room and their families. Those who ignore it will remain buried in paperwork.

References

1. Thirunavukarasu, A. J., et al. (2023). "Large language models in medicine." Nature Medicine.
2. Kung, T. H., et al. (2023). "Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models." PLOS Digital Health.
3. Lee, P., et al. (2023). "Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine." NEJM.