AI-Powered Chatbot Outperforms Medical Students in Clinical Care Exam Questions
An exciting new study conducted by researchers at Stanford University has demonstrated that ChatGPT, an advanced artificial intelligence (AI) system, surpasses the performance of first- and second-year medical students when it comes to answering complex clinical care exam questions. This groundbreaking research highlights the growing influence of AI in medical education and clinical practice, underscoring the need to reshape the approach to training future physicians.
ChatGPT, a well-known AI language model, has gained significant attention in recent months. These language models, which are trained on vast amounts of internet content, function as online chatbots, generating human-like responses based on user input.
Prior studies have already shown that ChatGPT can effectively handle multiple-choice questions from the United States Medical License Examination (USMLE), a crucial test for aspiring doctors. However, the Stanford researchers wanted to explore how this AI system performed with more challenging open-ended questions designed to assess the clinical reasoning abilities of first- and second-year medical students at the university.
Published in JAMA Internal Medicine, their study found that on average, ChatGPT scored over four points higher than the students in the case-report portion of the exam.
“We were astonished by ChatGPT’s exceptional performance on these free-response medical reasoning questions, surpassing the scores of human test-takers,” says Eric Strong, a hospitalist and clinical associate professor at Stanford School of Medicine and one of the study’s authors.
This new study utilized the latest version of ChatGPT, known as GPT-4, which was released in March 2023. The research builds upon a previous study that Strong and his colleague Alicia DiGiammarino led, which focused on the predecessor version, GPT-3.5, released by OpenAI in November 2022.
Testing Clinical Reasoning Skills with Realistic Patient Cases
For both studies, the Stanford researchers curated a set of 14 clinical reasoning cases. These cases consisted of descriptions ranging from several hundred to a thousand words, mimicking real patient medical charts with various extraneous details, like unrelated medical conditions and medications. Test-takers were required to provide paragraph-long responses to a series of questions following each case report.
This approach is in stark contrast to the relatively straightforward multiple-choice questions found in the USMLE. In those exams, test-takers are presented with a short passage, a question, and five potential answers, with all the relevant information provided.
“It’s not particularly surprising that ChatGPT and similar programs excel at multiple-choice questions,” explains Strong. “Test-takers are explicitly given the relevant information, mainly relying on recall. The real challenge lies in open-ended, free-response questions.”
However, before tackling the case-based questions, ChatGPT needed some assistance in understanding healthcare-specific terms used in the test. As ChatGPT draws information from the entire internet, healthcare terms such as “problem list” could be interpreted incorrectly and would require prompt engineering.
Once the questions were adjusted accordingly, the Stanford researchers fed the data into ChatGPT, recorded the chatbot’s responses, and had experienced faculty graders compare the AI program’s grades with those of the first- and second-year medical students.
In the previous study using GPT-3.5, the chatbot’s responses were considered “borderline passing,” according to Strong. However, in this new study using GPT-4, ChatGPT scored an average of 4.2 points higher than the students and achieved a passing rate of 93 percent, compared to the students’ 85 percent.
While ChatGPT’s performance is commendable, it is not without flaws. Confabulation, the addition of false details such as fever occurring where it did not, was an issue that improved significantly in GPT-4 compared to GPT-3.5. These “false memories” may result from ChatGPT conflating information from similar cases.
Redefining Medical Education for the AI Era
The impact of ChatGPT on test integrity and curriculum design is already being felt at Stanford School of Medicine. During the most recent semester, school administrators made the decision to switch from open-book exams, which allowed internet access including ChatGPT, to closed-book exams. This change means that students must rely solely on their memory to reason through questions. While this approach has its advantages, DiGiammarino points out that it no longer evaluates students’ ability to gather information—a crucial skill in clinical care.
Acknowledging this concern, the School of Medicine faculty and staff have formed an AI working group to explore modifications to the curriculum that integrate AI tools to enhance student learning. The ultimate goal is to ensure future clinicians are well-prepared to leverage AI in their practice.
“We don’t want doctors who solely rely on AI and struggle to reason through cases independently,” says DiGiammarino. “However, I am more apprehensive about a world where doctors aren’t proficient in effectively utilizing AI tools, considering their prevalence in modern medical practice.”
“We may still be decades away from the complete replacement of doctors,” adds Strong. “But incorporating AI into everyday medicine is just a few years down the road.”
Editor’s Notes: The Future of AI in Medical Education
The recent findings from Stanford University demonstrate the unparalleled capabilities of AI-powered chatbots like ChatGPT in the realm of medical education. Not only did it surpass the performance of medical students in answering challenging clinical care questions, but it also highlighted the need for an evolving approach to training future doctors. The integration of AI tools in medical education curricula has the potential to enhance students’ clinical reasoning abilities and better prepare them for the AI-driven future of healthcare.
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