AI in clinical care: Improving diagnosis, decisions and patient outcomes

Mr Mikael Edsbagge

Mikael Edsbagge is a Senior Consultant Neurologist, Deputy Head of Department of Neuromedical Care, and Digital Transformation Strategist at Sahlgrenska University Hospital in Gothenburg, Sweden. In addition, contributions are made to digital transformation initiatives across Region Västra Götaland, the public healthcare provider of western Sweden.

Areas of focus include digital health, patient empowerment, AI-supported healthcare, and innovation-driven service transformation. By combining clinical practice, healthcare leadership, and strategic digital development, work is conducted at the intersection of healthcare, innovation, and digital transformation, with a particular emphasis on implementing digitally enabled and scalable models of care that improve efficiency and patient-centered healthcare delivery.

Driven by the challenge of delivering sustainable healthcare in times of growing demands and workforce limitations, the goal is to develop and implement scalable, safe, and patient-centered digital solutions that enable healthcare professionals to support larger patient populations while empowering individuals to take a more active role in managing their own health.

Mr Jose Leal

Business Administrator with a specialization in Healthcare Auditing and more than eight years of experience in the management of high-cost diseases and catastrophic conditions. Experienced in reporting, monitoring, and analyzing healthcare data to support population health management and risk stratification. Skilled in implementing comprehensive care strategies, health risk management, and monitoring clinical and administrative indicators to improve quality of care and healthcare outcomes. Proficient in working with multidisciplinary teams, ensuring compliance with healthcare regulations, and optimizing care processes through data-driven decision-making. Experienced in the use of health information systems and the analysis of clinical data to support timely interventions, improve operational efficiency, and strengthen chronic disease management programs. Committed to continuous quality improvement, patient-centered care, and the development of strategies that enhance healthcare performance and promote better clinical outcomes.
The Silent Revolution of Clinical Data: Artificial Intelligence to Anticipate Complications in Chronic Diseases

Dr Jae-yeon Lee

Jae-yeon (Jamie) Lee, MD, is an Assistant Clinical Professor of Emergency Medicine at Stanford University School of Medicine. She serves as Medical Director of the Stanford Walk-In Clinic and Assistant Medical Director for the Department of Stanford Emergency Medicine, where she leads initiatives focused on improving patient access, operational efficiency, and innovative models of emergency care delivery.

Dr. Lee completed her emergency medicine residency at NewYork-Presbyterian Cornell-Columbia, where she served as Administrative Chief Resident, followed by a Stanford Emergency Medicine Administration Fellowship. She is currently completing an MBA at the UCLA Anderson School of Management.

Her academic and operational work centers on telemedicine, healthcare innovation, clinical operations, and care redesign. She has led and evaluated novel programs aimed at improving emergency department throughput, patient experience, and access to care, including virtual care pathways and technology-enabled models of emergency medicine. Her research interests include digital health, telehealth implementation, and the application of operational strategies to improve quality, efficiency, and equity in healthcare delivery.

Mr Wei Xuan Lim

Wei Xuan Lim is a Lead AI Engineer with experience spanning data analytics, data science, AI engineering, and cloud architecture. He specialises in leading the design and delivery of secure, scalable AI solutions that translate complex operational and business needs into practical technology outcomes. His work has included driving the development of production-grade generative AI systems and pioneering a secure air-gap-to-cloud data pipeline that was subsequently adopted across multiple sites as shared foundational infrastructure.

His contributions to innovation were recognised at the 2025 SBR Management Excellence Awards, where he and his team won Team of the Year and Innovator of the Year.

As a technical leader, Wei Xuan provides architectural direction, leads technical and code reviews, mentors engineers, and helps shape product roadmaps and technology decisions. He is passionate about bridging technology and business to drive responsible AI adoption, strengthen organisational capabilities, and deliver meaningful transformation at scale.

Outside of work, Wei Xuan is drawn to the outdoors — carving down snowy slopes on his snowboard and travelling to explore new places, immerse himself in nature, and discover landscapes and cultures far from the everyday.

Dr Puteri Norliza Megat Ramli

Dr Puteri Norliza binti Megat Ramli is a PhD candidate at Universiti Kebangsaan Malaysia (The National University of Malaysia) and a healthcare leader with extensive experience in health services management and hospital administration. She holds a Master of Science in International Health Services and Hospital Management from London South Bank University, United Kingdom, and previously served as Deputy Director of the National Cancer Institute (Institut Kanser Negara), Ministry of Health Malaysia.

Her research interests include lung cancer, incidental pulmonary nodules, artificial intelligence (AI), health economic evaluation, and technology adoption in healthcare. Her doctoral research examines the real-world implementation of AI-assisted detection of incidental pulmonary nodules on chest radiographs across Malaysian healthcare settings, encompassing assessments of diagnostic performance, economic value, and radiologist acceptance of AI-assisted diagnostic systems.

Drawing upon experience in healthcare leadership, cancer services, and academic research, Dr Puteri Norliza is committed to advancing evidence-based innovations that strengthen diagnostic safety, facilitate earlier identification of lung cancer, and support the sustainable and clinically acceptable integration of AI technologies into routine healthcare practice.

Dr Yen Ee Tan

Dr Tan Yen Ee is a medical microbiologist in the Department of Microbiology, Singapore General Hospital. She did her undergraduate medical training at the National University of Singapore and completed her specialist microbiology training at the Singapore General Hospital. A fellowship to further her training in medical mycology at the Clinical Mycology Reference Laboratory, Centre for Infectious Diseases & Microbiology Laboratory Services (CIDMLS), Westmead Hospital, New South Wales Australia was awarded to her in 2016 under the Health Manpower Development Plan (HMDP) Fellowship Programme.

She is also the Adjunct Assistant Professor at the Duke-NUS Graduate Medical School, where she contributed to the training in microbiology. Her research interests are in the development of relevant clinical diagnostics and medical mycology.