Summary
Kang Zhang always uses his role of a frontline physician to identify and address urgent and unmet medical needs as a common theme interwoven into his research. He has been working on developing tools and solutions to aid transforming healthcare delivery and biology in an evolving “bedside-lab-bedside closed-loop circuit.”
Main text
Kang Zhang
Macao University of Science and Technology
Biography
Kang Zhang, MD, PhD is a Chair Professor and Vice-Dean for Research, Director of Center for Biomedicine and Innovations at Macau University of Science and Technology (M.U.S.T). Dr. Zhang is also the Director, Zhuhai International Eye Center, Zhuhai People 's Hospital/First Affiliated Hospital of Macau University of Science and Technology. Dr. Zhang obtained his MD with magna cum laude honors from the Harvard Medical School and MIT joint MD program and his PhD in genetics from Harvard University. He did his postdoctoral training also at Harvard. He completed his residency in ophthalmology at Johns Hopkins University and his retina surgery fellowship at University of Utah. Prior to coming to MUST, he was the founding director of the Institute for Genomic Medicine, Professor of Ophthalmology, Nanoengineering, and Genetics at the University of California, San Diego. Among his honors include an elected fellow of AAAS, AIBME, Association of American Physicians, American Society of Clinical Investigation, Royal Society of Medicine, Royal Society of Chemistry; Burroughs Wellcome Clinical Scientist Award in Translational Research; the Ophthalmologist 100 World Power list, American’s Top Ophthalmologists, Clarivate Highly Cited Researchers in Cross-Field in 2019, 2020, 2021, and 2022. Dr. Zhang has published over 300 peer-reviewed manuscripts in top peer-reviewed journals covering a wide range of topics in ophthalmology, genetics, epigenetics, stem cells, nano-engineering and 3D printing, and artificial intelligence and clinical trials. His discovery on HTRA1 as a major susceptibility gene for age-related macular degeneration is listed as one of the “top-ten breakthroughs in 2006” by Science, and his work on deep-learning based disease diagnoses and treatment is listed as one of eight papers as “Best of 2018” by Cell.
Can you tell us a little bit about your work?
My PhD work with Drosophila developmental genetics at Professor Norbert Perrimon’s laboratory at Harvard Medical School and subsequent postdoctoral work in human genetics at Professors Kricket Seidman’s and Jonathan Seidman’s laboratory also at Harvard Medical School laid a good foundation for my human genetics work on identifying novel genes and pathways for blinding diseases in my early career. My next research field entry in stem cell and tissue engineering was a natural step towards using my developmental biology and genetics background in tissue repair and regeneration in mammalian systems including humans. Since 2010, I have been using big data generated by high-throughput technologies and machine learning to address big picture biology questions including the epigenetic aging clock and liquid biopsy for early cancer detection; this work also has led me to my current research focus, which is to integrate and interpret immense multi-modal and multi-dimensional data to paint a complete portrait and understanding of each and all medical and biological traits.
How do you use AI/ML in your research/clinical practice?
I use AI algorithms to automatically diagnose blinding eye diseases and utilize a chatbot to better triage incoming patients. I used deep learning and transformer-based approach to integrate and analyze multi-modal and multi-dimensional data.
What is one way in which AI/ML has impacted you outside of your work?
AI applications are everywhere and have impacted all of us positively and profoundly: self-driving cars are an example.
How has AI evolved in your field/practice and how do you think it will continue to evolve in the future?
AI has been evolving from the early phase of image-based diagnoses using computer vision to currently multi-modal integration-based tasks. The convergence of technologies in biology and medicine has brought unprecedented opportunities, yet integrating and interpreting immense multi-modal, multi-dimensional data are still challenging. I would expect to see many new discoveries and breakthroughs that will completely change the face of medicine and biology in the near future.
What are the biggest challenges you see to clinical implementation of AI/ML?
Isolated data islands in each hospital and privacy preservation.
What are the biggest discoveries that AI has brought to your field?
Solving 3D protein structure is one of them; others include applying deep learning and natural language processing to address and solve many complex medical tasks that are not possible by a human being.