Abstract:

Artificial intelligence is entering a new phase in healthcare, moving beyond conventional prediction and classification toward intelligent systems that can understand multimodal clinical data, support complex decision-making, and enable increasingly personalised care. Recent advances in generative AI, large language models, multimodal learning, explainable AI, federated learning, and autonomous clinical decision-support systems are redefining how healthcare data can be transformed into meaningful and actionable knowledge. This talk will explore the next frontier of AI in healthcare, with a particular focus on the transition from high-performing algorithms to clinically trustworthy, interpretable, and deployable intelligent systems. It will examine emerging applications in medical imaging, disease prediction, precision medicine, remote monitoring, clinical decision support, and AI-assisted healthcare workflows. Key challenges including data heterogeneity, bias, privacy, cybersecurity, model generalizability, explainability, and regulatory considerations will also be discussed. The presentation will further highlight how human–AI collaboration, responsible AI design, and rigorous clinical validation can bridge the gap between laboratory innovation and real-world healthcare impact. Looking ahead, the convergence of generative AI, multimodal intelligence, digital health, and personalised medicine has the potential to reshape healthcare delivery. The next frontier will therefore not be defined simply by more powerful algorithms, but by AI systems that are safe, transparent, adaptive, clinically meaningful, and capable of improving outcomes for both patients and healthcare professionals.