Ummara Mumtaz

Ummara Mumtaz, Awais Ahmed, Summaya Mumtaz

We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field.

Ummara Mumtaz, Summaya Mumtaz

Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure accountability after deployment. This study makes two contributions. First, it presents a cross-regulatory empirical analysis of 480 real-world AI incidents from the AI Incident Database (AIID), evaluating their alignment with post-deployment provisions in three major governance frameworks: the EU AI Act (Articles 72-73), the NIST AI Risk Management Framework (MANAGE and GOVERN functions), and the General Data Protection Regulation (GDPR Articles 22, 33-35). The results reveal substantial governance gaps across these frameworks, indicating persistent weaknesses in post-deployment accountability. Second, based on these findings, the study proposes the Proactive AI Governance Compliance Framework (PAGCF), a four-phase lifecycle methodology designed to shift governance from reactive incident response toward pre-deployment compliance assurance. The framework includes risk-stratified governance tiers, an implementation checklist linked to specific regulatory provisions, and a projected impact analysis that uses internal monitoring as a proxy for proactive governance capacity.