Professor Dr. Somyot Kaitwanidvilai
Dean of School of Engineering, King Mongkut’s Intstitute of Technology Ladkrabang
Engineering the Future of Healthcare: AI, Intelligent Systems, and the Next Generation of Biomedical Innovation
Abstract
Healthcare is entering a new era in which biomedical engineering is increasingly driven by the convergence of artificial intelligence (AI), intelligent systems, advanced sensing, robotics, connectivity, and data science. Around the world, the focus is shifting from conventional medical devices and isolated diagnostic tools toward connected, adaptive, and patient-centered systems that can continuously understand physiological conditions, support clinical decisions, and personalize healthcare interventions.
Several emerging directions are shaping this transformation. AI-assisted medical imaging is improving the detection, segmentation, and interpretation of abnormalities in radiology, pathology, ophthalmology, and other image-based specialties. Wearable and Internet of Medical Things technologies are enabling continuous monitoring of physiological signals outside traditional hospital environments, creating opportunities for early detection and preventive healthcare. Meanwhile, AI-based predictive models are increasingly being explored for risk stratification, disease progression, treatment response, and personalized medicine.
Another important frontier is the integration of AI with biomedical robotics and intelligent therapeutic systems. Surgical and rehabilitation robots, assistive technologies, smart prostheses, and precision robotic systems are becoming increasingly capable of adapting to patients and clinical environments. Digital twins and computational physiological models offer another promising direction by creating virtual representations of patients or organs that may support treatment planning, simulation, and individualized therapy. At the same time, multimodal and generative AI are opening new possibilities for integrating medical images, biosignals, clinical information, and other heterogeneous data into more comprehensive decision-support systems.
An example from our recent work demonstrates this transition from AI prediction toward intelligent therapeutic systems. We developed an AIoT-based Adaptive Neuro-Fuzzy Inference System for high-flow oxygen therapy, integrating SpO₂ and respiratory-rate information with a cloud-based intelligent model to recommend adjustments in oxygen flow and FiO₂. Simulation-based closed-loop evaluation demonstrated the potential of continuous intelligent adjustment compared with less frequent manual titration, while bench testing verified the accuracy of the oxygen-delivery hardware. This study also highlights an important lesson: an accurate AI prediction model is not automatically an effective controller; biomedical AI must be designed as part of a complete feedback, safety, and clinical-supervision architecture.
Looking forward, the greatest impact of AI in biomedical engineering may therefore come not from replacing clinicians, but from creating intelligent partnerships between humans, machines, and data. The next generation of biomedical innovation must combine technical performance with interpretability, safety, cybersecurity, clinical validation, ethical responsibility, and human-centered design. Ultimately, the challenge for biomedical engineers is to transform AI from an algorithm into a trustworthy healthcare system—connecting sensing to intelligence, intelligence to action, and engineering innovation to better patient outcomes.
Curriculum Vitae
Associate Professor Dr. Somyot Kaitwanidvilai is an academic and researcher at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, with extensive experience in intelligent control systems, robotics, artificial intelligence, machine vision, optimization, and advanced engineering applications. His multidisciplinary research provides a strong foundation for applications in biomedical engineering, particularly in precision robotics, intelligent sensing, medical automation, and AI-assisted systems.
He received his doctoral degree from the Industrial Systems Engineering (ISE) Program at the Asian Institute of Technology (AIT) under a Royal Thai Government full scholarship. He also holds Master’s and Bachelor’s degrees in Electrical Engineering from KMITL. His academic and professional background spans electrical engineering, automation, industrial systems, intelligent control, and applied artificial intelligence.
A major area of his research is the development of robust and intelligent control techniques for complex dynamic systems. His work includes optimization-based control, adaptive control, fuzzy systems, neural networks, particle swarm optimization, machine learning, and computer vision. These technologies are highly relevant to biomedical engineering applications requiring accurate motion control, real-time decision making, intelligent monitoring, and human–machine interaction.
His research has also addressed robotic manipulation and high-precision control. Recent work includes the development of robust control approaches for MIMO microsurgical manipulation systems, demonstrating the applicability of advanced control methodologies to precision medical and biomedical robotic systems. His broader experience in robotic arms, force and impedance control, autonomous systems, sensor fusion, and intelligent visual inspection further supports interdisciplinary research between engineering, robotics, and healthcare technologies.
Dr. Kaitwanidvilai has published extensively in international journals indexed in Web of Science and Scopus. His research covers robust control, robotics, artificial intelligence, machine learning, computer vision, smart manufacturing, intelligent surveillance, and sensing technologies. His work emphasizes translating theoretical engineering methods into practical systems capable of operating reliably in complex real-world environments.
In addition to research and teaching, he has served as a reviewer for international journals, a technical committee member and session chair for international conferences, and an external examiner for graduate programs at several universities.
His current interests include AI-enabled intelligent systems, precision robotics, advanced sensing and image analysis, adaptive and robust control, and the integration of engineering technologies into biomedical and healthcare applications. His interdisciplinary perspective aims to bridge control engineering, artificial intelligence, robotics, and biomedical engineering toward safer, smarter, and more effective technologies