Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide
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Takanobu Hirosawa
and
Taro Shimizu
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.
Acknowledgments
This study was made possible using the resources from the Department of Diagnostic and Generalist Medicine, Dokkyo Medical University.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Editorial
- The physical exam and telehealth: between past and future
- Review
- Features and functions of decision support systems for appropriate diagnostic imaging: a scoping review
- Mini Reviews
- The PRIDx framework to engage payers in reducing diagnostic errors in healthcare
- Tumor heterogeneity: how could we use it to achieve better clinical outcomes?
- Original Articles
- Factors influencing diagnostic accuracy among intensive care unit clinicians – an observational study
- Prevalence of atypical presentations among outpatients and associations with diagnostic error
- Preferred language and diagnostic errors in the pediatric emergency department
- Diurnal temperature variation and the implications for diagnosis and infectious disease screening: a population-based study
- What’s going well: a qualitative analysis of positive patient and family feedback in the context of the diagnostic process
- Assessing clinical reasoning skills following a virtual patient dizziness curriculum
- Interleukin-6, tumor necrosis factor-α, and high-sensitivity C-reactive protein for optimal immunometabolic profiling of the lifestyle-related cardiorenal risk
- Effect of syringe underfilling on the quality of venous blood gas analysis
- Short Communications
- How do patients and care partners describe diagnostic uncertainty in an emergency department or urgent care setting?
- Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide
- Letters to the Editor
- How to overcome hurdles in holding mortality and morbidity conferences on diagnostic error cases in Japan
- Medical history-taking by highlighting the time course: PODCAST approach
- Journal Reputation Factor
- Case Report
- Pre-analytical errors in coagulation testing: a case series
- Acknowledgement
- Acknowledgement
