Towards Explainable and Safe Systems for Health Data

The integration of artificial intelligence into healthcare holds immense potential to transform medical services through personalized care, early diagnosis, and more informed decision-making. However, despite significant advances in digital health technologies, the adoption of AI-driven systems remains limited, particularly in sensitive domains such as mental health, due to concerns around transparency, safety, and user trust. These concerns are compounded by the complexity of clinical language, the high stakes of decision-making, and the need for accountability in automated systems. This research addresses these challenges by investigating how Large Language Models (LLMs) and related techniques can be adapted to create explainable, interpretable, and safe health information systems. Positioned at the intersection of health informatics, Natural Language Processing (NLP), and Information Retrieval (IR), this work explores both the potential and limitations of LLMs in clinical contexts. While LLMs are effective at processing large textual datasets and producing fluent responses, their black-box nature, tendency to hallucinate, and associated privacy risks make them unsuitable for direct application in healthcare without significant adaptation.