ChatGPT and medical education: a new frontier for emerging physicians

Waisberg, Ethan; Ong, Joshua; Masalkhi, Mouayad; Zaman, Nasif; Kamran, Sharif Amit; Sarker, Prithul; Lee, Andrew G; Tavakkoli, Alireza

doi:10.36834/cmej.77644

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…ChatGPT has previously attempted these tasks, however did not yet perform at suitable levels to be used clinically [ 2 – 7 ]. Large language models such as ChatGPT can make errors in understanding the context of information, or provide outdated information, which further complicates the usage of these technologies in a clinical context [ 8 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Google DeepMind’s gemini AI versus ChatGPT: a comparative analysis in ophthalmology

Masalkhi,

Ong,

Waisberg

et al. 2024

Eye

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Section: Introductionmentioning

confidence: 99%

Google DeepMind’s gemini AI versus ChatGPT: a comparative analysis in ophthalmology

Masalkhi,

Ong,

Waisberg

et al. 2024

Eye

Self Cite

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“…Noteworthy, there is a sheer infinite number of further LLMs and several significant extensions to these existing approaches (Yang et al, 2019;Brown et al, 2020;Stiennon et al, 2020;Chen et al, 2021;Neelakantan et al, 2022;Ouyang et al, 2022). Thanks to the generality of language, these models have been applied in various applications, ranging from search engines and chat bots (Kelly et al, 2023), general problemsolving and knowledge extraction (Petroni et al, 2019), medical diagnosis (Hirosawa et al, 2023;Nori et al, 2023;Rao et al, 2023;Shea et al, 2023;Waisberg et al, 2023), education (Tack and Piech, 2022;Ausat et al, 2023;Firat, 2023), law (Cyphert, 2021;Perlman, 2022;Trozze et al, 2023) and robotics, as we will discuss in the next section.…”

Section: Natural Language Interface Systems and Large Language Modelsmentioning

confidence: 99%

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation

Lakhnati,

Pascher,

Gerken

2024

Front. Robot. AI

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In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

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“…In their commentary, ChatGPT and medical education: a new frontier for emerging physicians, Waisberg et al 23 described the benefits and pitfalls of using the artificial intelligence software, ChatGPT, as a medical education tool. They noted that as the use of AI rapidly increases, doctors will need to develop the skills to use and interpret AI in clinical settings while mitigating potential errors.…”

Section: Commentary and Opinionsmentioning

confidence: 99%