The last few years have seen an unprecedented rise in large language models (LLMs) like ChatGPT and GPT-4. Their fluency in medical language and capacity for reasoning mean they can be harnessed as valuable tools for rheumatology exam preparation—without threatening the clinician’s role.
Rheumatology Exam Performance by LLMs
In a recent study focusing on the Spanish MIR rheumatology exams, GPT-4 demonstrated impressive performance—achieving 93.7 % accuracy across 145 specialized rheumatology questions, compared to 66.4 % for ChatGPT. Importantly, clinical reasoning evaluations by multiple rheumatologists rated GPT-4 at a median of 4.67/5, compared to ChatGPT’s 4.5—indicating credible reasoning capability alongside raw answer accuracy.
Source: https://www.nature.com/articles/s41598-023-49483-6
Teaching Support, Not Replacement
Rather than positioning LLMs as replacements for teaching faculty, the data suggest their greatest value lies in reinforcement and scaffolding. GPT-4 can generate plausible explanations for selected answers, simulate challenging test scenarios, and provide rationales that students—and teaching physicians—can critique and refine. This aligns with research showing LLMs’ strength in medical question answering without matching clinician nuance.
Source: https://arxiv.org/abs/2305.09617
Simulated Clinical Reasoning Exercises
Beyond multiple-choice practice, LLMs have been proposed as virtual tutors for clinical reasoning development. In rheumatology-specific education, virtual patient (VP) simulations enhanced by LLMs—sometimes embodied in social robotic platforms—have shown to provide more authentic, interactive learning experiences than static digital cases. Students reported that such platforms improved hypothesis generation, adaptability, and conversational realism—critical to mastering complex rheumatologic presentations.
Source: https://link.springer.com/article/10.1007/s00296-024-05731-0
Advantages for Educators and Learners
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Scalable Question Banks – Educators can leverage LLMs to rapidly generate tailored practice questions with answer rationales—especially beneficial for less common rheumatic diseases.
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Automated Feedback – Learners receive immediate, written feedback, helping identify knowledge gaps—especially when faculty time is limited.
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Diverse Case Exposure – LLMs can simulate rare clinical scenarios, giving students exposure beyond typical cases seen in rotations.
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Active Learning – Interacting with an LLM encourages students to articulate reasoning, ask follow-up questions, and reflect—deepening understanding.
Limitations and Safeguards
However, blind reliance on LLM outputs poses risks:
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Hallucinations – Models like GPT-4 can “hallucinate”—provide plausible but incorrect or fabricated information.
Sources: https://en.wikipedia.org/wiki/GPT-4 and https://pmc.ncbi.nlm.nih.gov/articles/PMC11751060/ -
Incomplete Medical Context – LLMs may lack depth in evidence nuances or emerging treatment guidelines.
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Lack of Judgment – Subtle clinical judgment—e.g., weighing patient frailty or comorbidities—remains a clinician’s domain.
To mitigate these risks, all AI-generated content must undergo review by qualified rheumatologists. Incorporating LLMs into exam prep tools should always include expert validation, prompt oversight, and clear disclaimers about AI limitations.
Conclusion
LLMs like ChatGPT and GPT-4 represent powerful adjuncts for rheumatology exam prep—capable of delivering high accuracy in specialty questions, plausible explanations, and immersive virtual scenarios. Yet they are best viewed as companions to—not replacements for—clinician educators. Harnessed thoughtfully and ethically, LLMs can enrich medical education, strengthen clinical reasoning, and prepare learners without compromising the integrity of rheumatology training.