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KFSHRC Shines: Saudi Medical Talent Impresses at Global Competition

KFSHRC Shines: Saudi Medical Talent Impresses at Global Competition

Leaders09-04-2025

The King Faisal Specialist Hospital and Research Center (KFSHRC) showcased Saudi medical excellence at the Doctor's Dilemma competition in Louisiana. Sixty elite teams from leading US and Canadian medical schools competed at the American College of Physicians' annual meeting.
Led by internal medicine resident Dr. Fahad Al Jabir, the KFSHRC team delivered a dynamic scientific presentation. Their performance underscored Saudi Arabia's growing influence in global healthcare, spotlighting the skills of homegrown professionals.
Dr. Al Jabir praised Saudi leadership for prioritizing healthcare and empowering local talent. 'This experience highlights our nation's commitment to excellence,' he said, linking the achievement to Vision 2030's goals. KFSHRC Clinches Top Regional, Global Rankings
KFSHRC holds impressive global and regional rankings, earning first place in the Middle East and Africa on the 2025 Top 250 Academic Medical Centers list. Globally, it secured 15th position, reinforcing its status as a healthcare pioneer.
Meanwhile, Brand Finance named it Saudi Arabia and the Middle East's most valuable healthcare brand in 2024. Additionally, Newsweek also ranked it among the world's top smart hospitals for 2025, cementing its innovative reputation.
KFSHRC's global recognition aligns with Saudi Arabia's push to lead in medical innovation. These milestones strengthen the Kingdom's position as a rising hub for world-class healthcare and research.
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How emerging AI talent is shaping the future of smart healthcare in Saudi Arabia
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How emerging AI talent is shaping the future of smart healthcare in Saudi Arabia

RIYADH: As Saudi Arabia accelerates its investment in AI-powered healthcare, two young researchers from the Mohamed bin Zayed University of Artificial Intelligence are building the very tools that hospitals in the Kingdom will soon need — intelligent, interpretable, and scalable systems for diagnosis and prognosis. Although the university's 2025 cohort did not include Saudi nationals this year, the work of two standout graduates, Mohammed Firdaus Ridzuan and Tooba Tehreem Sheikh, directly aligns with Saudi Arabia's healthcare transformation plans under Vision 2030. Their research offers practical, forward-looking solutions for the Kingdom's next generation of smart hospitals. At a time when AI systems are being deployed across diagnostic units in Saudi hospitals, from the King Faisal Specialist Hospital to new initiatives backed by the Saudi Data and AI Authority, the focus is shifting from capability to clarity. Can the systems provide real-time support? Can they explain their reasoning? Can doctors intervene? These are the questions both Ridzuan and Sheikh have set out to answer. Ridzuan, a PhD graduate in machine learning, developed Human-in-the-Loop for Prognosis, or HuLP for short — a cancer survival prediction system that places doctors back at the center of AI-powered decision-making. 'While AI has made significant strides in diagnosing diseases, predicting individual survival outcomes, especially in cancer, is still a challenging task,' Ridzuan told Arab News. 'Our model addresses this by enabling real-time clinician intervention.' Unlike traditional models that operate in isolation, HuLP is built for collaboration. Medical professionals can adjust and refine its predictions using their clinical expertise. These adjustments are not just temporary; they influence how the model evolves. 'Doctors and medical professionals can actively engage with the system,' Ridzuan said. 'Their insights don't just influence the result — they actually help the model learn.' This approach to human-AI partnership ensures that predictions remain explainable, context-aware, and grounded in patient-specific realities, a key need for Saudi hospitals integrating AI at scale. 'By allowing clinicians to dynamically adjust predictions, we create a more adaptive and responsive system that can handle local challenges,' Ridzuan added. The Kingdom's healthcare institutions are undergoing a digital transformation driven by national entities like SDAIA, the Ministry of Health, and the Center for Artificial Intelligence in Medicine and Innovation. These entities are focused not only on adopting new AI tools but also on ensuring that these systems can integrate into clinical workflows. This is where Ridzuan sees HuLP making an impact. 'Smart hospitals are already integrating AI diagnostic tools for medical imaging and patient data analysis,' he said. 'Our model can take this to the next level by empowering clinicians to interact with and guide the system's predictions.' In settings where trust and transparency are vital, Ridzuan's collaborative model could help hospitals overcome one of AI's most persistent problems: the black box effect. This refers to the opaque nature of certain systems, particularly in the field of AI, where the internal workings and decision-making processes are hidden or unknown. The emphasis on local relevance also comes through in HuLP's design. Ridzuan says real-time data from regional healthcare systems is essential for training accurate, context-sensitive models. 'Local data provides insights into the unique health conditions and medical practices within the Gulf region,' he said. 'Integrating this data ensures that the AI is attuned to the specific needs and health profiles of patients in the region.' The system is built to learn continuously. As clinicians correct or refine its predictions, the model updates itself, improving with each interaction. This feedback loop is crucial for real-world deployments, especially in the Gulf, where data quality can be inconsistent. While Ridzuan is focused on outcomes, Sheikh, an MSc graduate in computer vision, is transforming the way hospitals detect disease in the first place. Her project, Med-YOLOWorld, is a next-generation imaging system that can read nine types of medical scans in real time. Unlike traditional radiology AI tools, which are often limited to specific tasks, Med-YOLOWorld operates with open-vocabulary detection. That means it can identify anomalies and organ structures that it has not been explicitly trained on — a key feature for scalability. 'Most models are confined to a single modality like CT or X-ray,' Sheikh told Arab News. 'Med-YOLOWorld supports nine diverse imaging types, including ultrasound, dermoscopy, microscopy, and histopathology.' 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