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https://doi.org/10.4...arrow_drop_down
https://doi.org/10.4018/407369...
Part of book or chapter of book . 2026 . Peer-reviewed
Data sources: Crossref
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Applications and Challenges of Generative AI in the Medical Field

Authors: Zhenchao Tao; Lei Li;

Applications and Challenges of Generative AI in the Medical Field

Abstract

The medical field is one of the important vertical application areas for generative artificial intelligence (AI), which has been employed in disease diagnosis, disease prediction, and clinical research, bringing tremendous opportunities for the demand and development of medical data and providing convenience for patients, healthcare professionals, AI practitioners, and regulatory authorities. However, the integration of generative AI in the medical field presents numerous challenges related to risk perception, value shaping, organizational transformation, institutional change, and policy response. Therefore, the rapid and extensive application of generative AI will inevitably raise more ethical issues, and effective regulation will create a favorable environment for its development. While generative AI is expected to enhance healthcare standards, it is crucial to focus on the responsible use and regulation of AI technology to prevent misuse and unethical behavior and ensure the full protection of human dignity.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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