Clinical Med students’ validation of Arkangel AI: Are their responses any better when supported by the AI?

Abstract

ABSTRACT Introduction Large Language Models (LLMs) in healthcare practice and education have been evaluated using medical question-answering (QA) datasets, with excellent performance. However, multiple-choice questions fall short when assessing more complex language interactions. Objective To evaluate the time invested and validity of medical students’ responses to clinical questions using ArkangelAI, compared to traditional search methods. Methods Randomized, double-blind trial with clinical medical students assigned to two groups. Each group answered four clinical questions from each of four clinical cases, one using ArkangelAI, the other using traditional research methods: Google, PubMed, etc. Field specialists evaluated the responses using six pre-established criteria to define the answers’ validity. Total average validity (the mean of individual scores) was compared by groups with hypothesis testing and 95% CI. The time to respond was also compared. Results Eighty-three medical students were randomized to groups A (43) and B (40). Average differences responded in half the time (three minutes faster) than the control group, with 98% fewer searches needed. The model’s answers were valid (accurate, non-biased, aligned with consensus, and safe) with a total validity score of 2.84 (group A) and 2.69 (group B). Most Arkangel AI users found it helpful for daily practice and would recommend it to colleagues. Conclusion: LLM-supported methods appear to have a positive influence on effective clinical search without sacrificing, and even augmenting, the quality of answers. This is applicable at the clinical medical student level and for non-critical clinical reasoning. Validations, including graduated physicians and specialists, are needed to further understand the effect of LLMs in education and clinical practice.

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