Challenges for implementing generative artificial intelligence (GenAI) into clinical healthcare.

Area of Science:

• Artificial Intelligence
• Deep Learning
• Healthcare Technology

Background:

• Generative artificial intelligence (GenAI) is a sophisticated deep learning AI.
• GenAI demonstrates strong capabilities in natural language processing and data synthesis.
• Its potential applications in healthcare are extensive, offering scalability and cost-effectiveness.

Purpose of the Study:

• To explore the potential of GenAI in healthcare.
• To identify and analyze the challenges associated with GenAI implementation in clinical settings.
• To inform clinical experts on this evolving technology.

Main Methods:

• Review of evidence and expert opinion.
• Exploration of technological, regulatory, workforce, and trust-related issues.
• Synthesis of current understanding and future considerations.

Main Results:

• GenAI possesses versatile capabilities beneficial for healthcare, including advanced communication and data analysis.
• Significant challenges impede widespread adoption, such as technical hurdles, regulatory frameworks, workforce integration, and establishing user trust.
• The technology's potential is substantial but contingent on overcoming these implementation barriers.

Conclusions:

• GenAI presents a transformative opportunity for the healthcare sector.
• Addressing implementation challenges is crucial for realizing GenAI's full potential in medicine.
• Continuous evaluation and expert guidance are necessary for navigating GenAI's integration into healthcare.