Generative AI and Explainable AI in the Field of the Health Industry: Application, Ethical Considerations and Future Challenges
Keywords:
GENAI, XAI, LLMAbstract
The emergence of Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) represents a paradigm shift in the digital transformation of the healthcare industry. GenAI models, including generative adversarial networks (GANs), variational autoencoders (VAEs) and large language models (LLMs), can generate synthetic yet realistic medical data. This facilitates innovations in medical imaging, drug discovery and personalized treatment planning. XAI focuses on ensuring the transparency, interpretability and trustworthiness of AI models. The most important factors in clinical applications are where human lives are at risk. The integration of GenAI and XAI has the potential to improve clinical decision support systems, optimize workflows, and enhance healthcare outcomes, while addressing critical issues such as data scarcity, bias, and patient privacy. However, combining them also presents substantial technical, ethical, and regulatory challenges, including the need for interpretable generative models, adherence to data protection laws such as the General Data Protection Regulation (GDPR), and ensuring fairness and accountability in automated decision-making. This paper explains a comprehensive analysis of the applications and implementation frameworks of GenAI and XAI in healthcare, emphasizing their role in fostering trustworthy, transparent and patient-centered AI solutions.
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