Generative AI and Large Language Models in Orthopaedics

Orthopaedic surgery has always been a specialty defined by its tools. From the invention of the Thomas splint to the development of the locking plate and the advent of robotic-assisted arthroplasty, we are a profession that embraces technology to solve complex biomechanical and clinical problems. However, while Computer Vision and robotics have steadily transformed what we do in the operating theatre, Generative Artificial Intelligence (AI) and Large Language Models (LLMs) are fundamentally rewiring everything we do outside of it.

Large Language Models like OpenAI’s GPT-4, Anthropic’s Claude 3, and Google’s Med-PaLM have exploded onto the scene, offering a level of text understanding, synthesis, and generation that was considered pure science fiction just a few years ago. For the orthopaedic surgeon—who often spends up to 50% of their working hours on clinical documentation, coding, and administrative tasks—this technology offers the tantalizing promise of liberation.

But beyond simply saving time in the clinic, generative AI is rapidly emerging as an indispensable tool for orthopaedic surgery training, fellowship exam preparation, and complex clinical decision-making. As we navigate this paradigm shift, it is critical to understand both the immense power and the inherent risks (such as clinical hallucination and data privacy breaches) of integrating the "Silicon Resident" into our daily practice.

What is Generative AI?

To understand the current AI revolution, it is helpful to distinguish between two broad categories of artificial intelligence. Traditional or "Discriminative AI" is designed to classify data. It looks at a pelvic radiograph and answers a binary or categorical question: "Is there a fracture?" or "No fracture." It discriminates between states.

Generative AI, on the other hand, creates entirely new data. At its core, an LLM is a highly sophisticated prediction engine. It has ingested a vast corpus of human knowledge—the sum total of the internet's text, including medical journals, textbooks, and forums—and uses billions of parameters to predict the next most logical word in a sequence.

When these foundational models are specifically fine-tuned on high-quality medical literature (such as Google's Med-PaLM 2 or specialized healthcare instances of GPT-4), the results are staggering. These models routinely pass the USMLE and orthopaedic board-style questions with flying colors. More importantly, they can reason through complex, multi-step clinical vignettes, synthesize conflicting information, and generate nuanced management plans. They have evolved from simple search engines into dynamic reasoning engines.

Clinical Applications: Transforming the Orthopaedic Workflow

The integration of LLMs into clinical practice is already happening. Here is how generative AI is currently reshaping the orthopaedic landscape.

1. The Automated Scribe (Ambient Clinical Intelligence)

Ask any orthopaedic surgeon what contributes most to their burnout, and the answer is universally the Electronic Medical Record (EMR). Ambient Clinical Intelligence is the "Killer App" that directly addresses this pain point.

• The Workflow: The surgeon walks into the clinic room. A secure, HIPAA-compliant app on their smartphone (such as Nuance DAX Copilot or Nabla Copilot) actively listens to the natural conversation between the surgeon and the patient.
• The Magic: The AI is smart enough to filter out the small talk ("How was the drive in?", "How is your golf game?"). It extracts the relevant History of Present Illness (HPI), organizes the physical exam findings (which the surgeon dictates aloud during the exam), and synthesizes the agreed-upon Assessment and Plan.
• The Output: Within seconds of leaving the room, the system generates a perfectly formatted, grammatically correct clinic letter: "Mr. Smith is a 65-year-old male presenting with a 6-month history of progressively worsening right medial-sided knee pain, consistent with osteoarthritis..." It can even automatically generate the appropriate billing codes.
• The Benefit: Surgeons are saving an average of 2 to 3 hours of typing and dictation per day. More importantly, the computer screen is removed as a barrier; eye contact and the human connection are restored to the doctor-patient relationship.

2. Patient Communication and Health Literacy Translation

"Doctor, the MRI report says I have advanced tricompartmental chondromalacia and a complex degenerative tear of the posterior horn of the medial meniscus with parameniscal cyst formation. Am I going to lose my leg?"

Orthopaedic terminology is notoriously dense. Patients often read their own radiology reports via patient portals before they even see the surgeon, leading to profound anxiety.

• Translation: LLMs are incredible translators—not just between English and Spanish, but between "Medicalese" and "Plain English."
• Custom Instructions: You can securely prompt an AI: "Rewrite this MRI report and my operative plan as a compassionate letter to the patient, explaining their condition at a 6th-grade reading level. Emphasize that this is a common wear-and-tear issue and that joint replacement is highly successful."
• Intelligent Triage Chatbots: Large health systems are deploying AI chatbots to triage post-operative questions. When a patient asks, "Is this amount of wound drainage normal on day 4 after my total hip?", the AI can assess the text description (and increasingly, a securely uploaded photo) to advise either "This is expected serosanguinous drainage, continue to monitor" or "Please present to the Emergency Department for evaluation."

3. The "Silicon Resident" (Clinical Decision Support)

While not yet FDA-approved to make autonomous diagnoses, LLMs act as a remarkably powerful, always-available sounding board for complex clinical scenarios.

• Complex Case Formulation: Imagine you are a registrar on a busy night shift. You can input a de-identified scenario: "I have a 45-year-old male with a history of renal transplant on immunosuppressants, presenting with acute onset groin pain. X-rays are normal. Inflammatory markers are mildly elevated. What is the differential diagnosis, and what are the specific criteria for the next diagnostic steps?" The AI will instantly formulate a structured differential including AVN, transient osteoporosis of the hip, occult stress fracture, and atypical septic arthritis, while suggesting an urgent MRI and citing relevant guidelines.
• Guideline Adherence: Guidelines change frequently. Instead of digging through PDFs, you can ask: "What are the current 2024 AAOS guidelines for VTE prophylaxis in a patient undergoing total ankle arthroplasty with a prior history of a DVT?" The AI retrieves and synthesizes the specific, up-to-date protocol instantly.

The LLM Advantage in Fellowship Exam Preparation

For orthopaedic surgery trainees preparing for high-stakes fellowship exams (FRACS, FRCS Tr & Orth, ABOS Part I and II), LLMs are revolutionizing how we study. Passive reading of textbooks is out; active, AI-assisted recall is in.

1. Generating Custom Multiple Choice Questions (MCQs)

You can upload a classic paper or copy text from an orthopaedic textbook and prompt the LLM: "Create 5 complex, board-style multiple-choice questions based on this text regarding the management of developmental dysplasia of the hip (DDH). Include one correct answer and four plausible distractors. Provide a detailed explanation for why the correct answer is right and why each distractor is wrong."

2. Viva / Oral Exam Simulation

The most difficult part of exam prep is practicing the oral defense. You can use an LLM's voice mode (like ChatGPT's advanced voice feature) to act as your examiner.

3. Summarizing Landmark Papers

Fellowship exams require an intimate knowledge of landmark literature. Instead of spending hours reading full texts, you can use AI to synthesize them. Prompt the AI to summarize the SPORT trial for lumbar disc herniation or the SPRINT trial for tibial shaft fractures, specifically asking it to extract the inclusion criteria, primary outcomes, and limitations into a quick-reference table.

Research and Academic Productivity

The "publish or perish" culture in surgical education places a heavy burden on trainees and attendings alike. Generative AI significantly accelerates the mechanical aspects of research.

Data Extraction for Systematic Reviews

• The Old Way: Manually reading 500 abstracts on PubMed, entering data line-by-line into a massive Excel spreadsheet to see if they fit your inclusion criteria.
• The New Way: Feed the exported abstracts into an LLM via an API with a precise prompt: "Review these 500 abstracts. Extract the study design, sample size, minimum follow-up time, and deep infection rate. Output the results in a CSV format." What took weeks now takes minutes.

Drafting and Formatting

LLMs are exceptional at overcoming "blank page syndrome." They can rapidly draft an introduction, format your references to match a specific journal's style guide (e.g., changing from APA to AMA instantly), and polish your grammar to ensure professional academic prose.

The Risks: Hallucination and Data Privacy

As with any powerful surgical tool, improper use can lead to catastrophic complications. The risks of generative AI in medicine fall into two main categories.

1. Clinical Hallucinations

LLMs are designed to be highly plausible, not inherently truthful. Because they predict the next best word, they can confidently invent clinical guidelines, fabricate entire classification systems, or cite peer-reviewed papers that simply do not exist.

For example, if you ask an LLM about the "Smith-Jones classification for distal radius fractures," it might invent a highly detailed, completely fake 4-part classification system rather than admitting it doesn't know.

The Golden Rule: Never trust an LLM for critical clinical decision-making without independently verifying the source text. Trust, but aggressively verify.

2. Data Privacy (HIPAA/GDPR Compliance)

When you type information into a public model (like the free version of ChatGPT or Claude), you are often granting the company permission to use that data to train future versions of the model.

The Platinum Rule: NEVER paste Protected Health Information (PHI)—including patient names, MRNs, dates of birth, or highly specific clinical identifiers—into a public chatbot. Only use enterprise-grade, secure "walled garden" instances that have signed a Business Associate Agreement (BAA) with your hospital system, ensuring your data is not retained or used for training.

Prompt Engineering for the Orthopaedic Surgeon

An LLM is only as good as the instructions you give it. Vague prompts yield vague, generic answers. "Prompt engineering" is the skill of structuring your request to get the exact output you need.

For clinical and academic tasks, utilize the R-C-O (Role, Context, Output) framework:

• R (Role): Tell the AI who it is. "Act as an expert, fellowship-trained orthopaedic hip and knee arthroplasty surgeon and senior academic editor."
• C (Context): Provide the specific background. "I am an orthopaedic registrar writing a letter to a primary care physician regarding a 72-year-old patient with severe bone-on-bone osteoarthritis of the right hip. The patient has multiple medical comorbidities (HbA1c 8.5, BMI 42) and is currently deemed too high-risk for elective surgery."
• O (Output): Define exactly what you want. "Produce a concise, professional, and empathetic clinic letter. Do not use overly complex jargon. Clearly outline the strict targets for optimization (HbA1c < 7.5, BMI < 40) required before we can safely offer a Total Hip Arthroplasty, and suggest non-operative modalities in the interim. Use bullet points for the optimization targets."

By structuring your prompts this way, you force the AI to adopt the correct tone, adhere to your clinical reasoning, and format the text exactly as you need it for your EMR.

Future Trends: Autonomous Robotics and Embodied AI

Where do Large Language Models meet the physical reality of the operating room? The answer lies in Embodied AI and Vision-Language Models (VLMs).

Current robotic systems in orthopaedics (such as the Stryker Mako or Zimmer Biomet ROSA) are largely "Cobots"—collaborative robots that keep the surgeon rigidly within a pre-operative 3D plan but still require the surgeon's hand to drive the saw or burr. They are highly precise but fundamentally lack "understanding" of the soft tissue envelope.

The next generation of AI will combine the reasoning capabilities of LLMs with real-time computer vision.

• The Concept: An AI model ingests and analyzes 10,000 hours of unedited surgical video of total knee arthroplasties. It doesn't just learn where the bone cuts go; it learns to recognize the subtle tension of the medial collateral ligament, the optimal sequencing of releases, and the visual cues of correct patellar tracking.
• The Future: We are moving toward a future where a robotic system, supervised by a surgeon, can autonomously perform soft tissue dissection, dynamically balance a joint based on real-time haptic feedback, and even perform autonomous closure. The surgeon transitions from the primary manual operator to a strategic overseer and systems manager.

Conclusion

Generative AI is the "bicycle for the mind." It is the most significant technological leap in medical informatics since the digitization of the radiograph. It profoundly amplifies the orthopaedic surgeon's ability to communicate clearly, document efficiently, and reason through vast amounts of medical literature.

We are entering an era where AI proficiency will be just as critical to surgical practice as knowing your surgical anatomy. Those who actively embrace and learn to wield these tools will find themselves liberated from the keyboard, with more time to dedicate to the operating room, their surgical education, and their families. Those who ignore it will simply remain buried in paperwork, competing against peers who have augmented their practice with the power of the Silicon Resident.

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.
4. Meskó, B., & Topol, E. J. (2023). "The imperative for regulatory oversight of large language models (or generative AI) in healthcare." npj Digital Medicine.