Generative AI for Healthcare: Applications, Challenges, and Ethical Considerations

Generative AI for Healthcare: Applications, Challenges, and Ethical Considerations

Ram Sharma , Shristi Sethia , Dr. Goldi Soni*

3rd Year B.tech CSE, Amity School Of Engineering & Technology, Amity University Chhattisgarh

3rd Year B.tech CSE, Amity School Of Engineering & Technology, Amity University Chhattisgarh

*Assistant Professor, Amity School Of Engineering & Technology, Amity University Chhattisgarh

Abstract

Generative Artificial Intelligence (AI) is rapidly transforming the healthcare sector, offering novel approaches to medical imaging, drug discovery, personalized medicine, and data privacy through the generation of synthetic datasets. This paper explores the applications, challenges, and ethical considerations surrounding the use of generative AI in healthcare. Key applications of this technology include enhancing diagnostic capabilities by generating high-quality medical images, accelerating the drug discovery process by simulating chemical compounds, and tailoring treatment plans through personalized medicine. Generative AI's ability to create synthetic patient data also provides a promising solution for safeguarding patient privacy while advancing medical research.

However, the integration of generative AI into healthcare is met with several challenges. These include data quality issues, which can compromise the accuracy and reliability of AI-generated outputs, and the black-box nature of many AI models, making it difficult for healthcare professionals to fully understand or trust the systems. Moreover, the technical limitations, such as high computational costs and the difficulty of integrating AI with existing healthcare infrastructure, pose additional barriers to widespread adoption.

The ethical considerations of generative AI in healthcare are equally significant. Concerns over patient privacy and data security remain central, particularly when synthetic data is generated and used for research purposes. Furthermore, the potential for algorithmic bias to influence healthcare outcomes raises questions about fairness and equity in AI-driven decisions. Establishing clear lines of accountability and ensuring that AI systems comply with existing regulatory frameworks are essential for building trust and safeguarding patient well-being.

Looking forward, the paper highlights the importance of developing explainable AI systems that offer greater transparency and integration with human decision-making processes. Future advancements in personalized medicine and drug discovery will rely on cross-disciplinary collaboration between AI researchers, healthcare professionals, and policymakers. Ultimately, the paper emphasizes that while generative AI holds tremendous potential for revolutionizing healthcare, its success will depend on addressing both the technical and ethical challenges it presents.

Keywords: Generative Artificial Intelligence, Medical Imaging, Healthcare, Personalized Medicine, Synthetic Datasets, Diagnostic Capabilities, Patient Privacy, Explainable AI, Algorithmic Bias