Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide
9
Zitationen
2
Autoren
2023
Jahr
Abstract
Abstract Objectives This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians. Methods We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports. Results Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension. Conclusions The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians’ diagnostic skills, heralding a transformative era in digital healthcare.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.700 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.605 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.133 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.873 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.