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Generative Artificial Intelligence (GEN AI) systems can create images, text, and content based on human-provided inputs. Academics and businesses have concentrated on Generative Artificial Intelligence (GEN AI) models such as ChatGPT, DALL-E, and Bard, which possess numerous applications, especially in healthcare. GEN AI has the potential to revolutionize medical research, diagnosis, therapy, and patient care through practical applications. Healthcare applications have not been thoroughly examined despite their considerable potential. This study investigates the possibility of ChatGPT and DALL-E to enhance medical imaging, drug discovery, personalized treatments, clinical trials, mental health support, and more applications. Data privacy, bias, interaction with existing systems, and decision-making capabilities are significant impediments. The study emphasizes the necessity of tackling these challenges. It suggests risk mitigation strategies to guarantee AI’s ethical, secure, and efficient integration in healthcare while also analyzing opportunities and risks to guide future research and offer practical insights for healthcare stakeholders.

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GEN AI in the Healthcare Industry: An Analysis of Privacy and Security Challenges, Emerging Models, Applications, and Limitations

  • 会议论文

  • 发表日期：

    2026年

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  • Ankit Srivastava,
  • Sandipan Bhowmick,
  • Munesh Chandra,
  • Ashim Saha

摘要

Generative Artificial Intelligence (GEN AI) systems can create images, text, and content based on human-provided inputs. Academics and businesses have concentrated on Generative Artificial Intelligence (GEN AI) models such as ChatGPT, DALL-E, and Bard, which possess numerous applications, especially in healthcare. GEN AI has the potential to revolutionize medical research, diagnosis, therapy, and patient care through practical applications. Healthcare applications have not been thoroughly examined despite their considerable potential. This study investigates the possibility of ChatGPT and DALL-E to enhance medical imaging, drug discovery, personalized treatments, clinical trials, mental health support, and more applications. Data privacy, bias, interaction with existing systems, and decision-making capabilities are significant impediments. The study emphasizes the necessity of tackling these challenges. It suggests risk mitigation strategies to guarantee AI’s ethical, secure, and efficient integration in healthcare while also analyzing opportunities and risks to guide future research and offer practical insights for healthcare stakeholders.