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Generative Artificial Intelligence in Dermatology: A Primer.

Jonathan Kantor1

  • 1Department of Medicine, Florida State University College of Medicine, Tallahassee, FL 32304, USA; Center for Global Health; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Engineering Science, University of Oxford, Oxford, UK.

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This summary is machine-generated.

Advanced generative artificial intelligence (AI) models are transforming medicine. Understanding how these AI systems function and their potential risks is crucial for responsible clinical application and improved patient care.

Keywords:
Artificial intelligenceChatGPTEpidemiologyGeminiGenerative artificial intelligenceMachine learningNeural networks

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • The rapid advancement of transformer-based generative artificial intelligence (AI) models is significantly impacting the medical field.
  • These AI systems have the potential to influence various aspects of healthcare, including medical education and surgical decision-making.

Purpose of the Study:

  • To elucidate the fundamental workings of advanced generative AI models in medicine.
  • To explore the responsible application of these AI systems for enhancing patient care.
  • To highlight the importance of understanding potential risks associated with AI implementation.

Main Methods:

  • Discussion of the core principles behind transformer-based generative AI.
  • Exploration of prompt engineering as a method for clinician customization.
  • Analysis of potential direct and indirect risks of AI in clinical settings.

Main Results:

  • Generative AI models offer transformative potential across the medical landscape.
  • Prompt engineering enables clinicians to tailor AI tools for specific needs.
  • Awareness of AI's potential risks is paramount for safe integration.

Conclusions:

  • Clinicians, educators, and researchers must understand both the capabilities and risks of generative AI.
  • Responsible AI adoption requires a thorough appreciation of its underlying mechanisms and potential consequences.
  • The integration of AI into healthcare necessitates a proactive approach to risk management and ethical considerations.