Artificial Intelligence (AI) is at the cusp of transforming every industry in profound ways by creating a seamless partnership between man and machine. Professor Chalapathy Neti is particularly excited by the potential of AI to transform biomedical research and healthcare. After a long stint at IBM in developing AI technologies and applying it to healthcare and education, Professor Neti is convinced that it can profoundly impact and fast track our journey towards precision medicine. His desire and ambition are to help expedite the development of a “scalable” federated AI platform for early detection and intervention of diseases like cancer, anchored on multi-omics/multi-scale data – an exemplary implementation of the “Cancer Moonshot”. While several efforts are underway, they are still piecemeal, and fraught with many challenges at the technical, social and policy level.
Deploying AI at scale is a challenge and requires significant innovation in federated data architectures that ensure data privacy, security, and intellectual property needs. In this talk, Professor Neti will describe a systematic approach to design and build a federated AI and data architecture on Hybrid Cloud to enable AI at scale and tackle grand challenges like the “Cancer Moonshot”.
For the AI layer, Professor Neti will describe a hierarchical deep learning approach that combines disease progression signatures at multiple scales (molecular, cell-level, tissue-level, organ-level, body-level, and population-level data) for early detection and intervention of cancer progression. For the data layer, he will describe the concept of a comprehensive cancer atlas i.e. a rich multimedia database spanning structured data (clinical attributes/symptoms), text (genetic signatures, clinical notes), and medical images (cytopathology and histopathology of tumors, CT images, etc.). Several efforts are underway to create such atlases (e.g. TCGA, NCI Cancer research Data Commons; International Cancer Proteogenomic consortium, etc.) to advance research.
Professor Neti will discuss the daunting challenges of creating such cancer atlases for federated AI. Data that underpins the care progression for any individual typically spans several years and is distributed across many healthcare institutions (primary, secondary, and tertiary) and departmental silos within an institution (clinical, radiology, pathology, etc.). He will outline the design of the cancer atlas using a federated data architecture on hybrid cloud, to enable a dynamic, longitudinal, secure and privacy preserving (w/ patient in control) architecture. If the cancer atlas is organized appropriately using FAIR (Findable, Accessible, Interpretable, Reusable) principles, linked at multiple levels, like concepts in Wikipedia, and zoomable like “Google maps” (from body-level to molecular-level, similar to satellite to individual houses in Google maps), it will enable and unleash unprecedented use of AI/Machine learning research for disease progression modeling, accurate clinical trial matching for biomarker based clinical trials, and other use cases.
Professor Chalapathy Neti is a seasoned senior executive with deep experience in AI and its application to healthcare, education and customer intelligence. He held various roles as the Vice President, Artificial Intelligence, Asia-Pacific, IBM Global Markets; as VP, IBM Watson Education, responsible for developing a specialized AI platform for personalized learning and incubating and leading an AI business; and as Director of Healthcare Transformation, responsible for IBM’s big bet (a $100m investment) on “Healthcare Transformation”. He built a cohesive worldwide R&D effort on AI for evidence-based medicine that culminated in the launch of IBM Watson Health business; laid the foundation for offerings like Watson Genomics and Smarter Care offerings, and set up strategic R&D partnerships with Memorial Sloan Kettering Cancer Center, Mayo Clinic and Cleveland Clinic, Pearson, Sesame Workshop and others.
Professor Chalapathy Neti received his Ph.D. degree in Biomedical Engineering from The Johns Hopkins University, and B.S degree from Indian Institute of Technology, Kanpur. He has authored over 75 scientific publications and 25 patents in various fields related to bio-medical informatics, medical imaging, speech and video analysis, Deep learning, Computational Neuroscience and VLSI Design.
He is passionate about the use of AI in transforming healthcare and education. He is an active contributor to discourse on evidence-based medicine and personalized learning. He was a contributor to Institute of Medicine (US National Academy of Sciences) on Evidence-based Medicine and Learning Healthcare Systems; a co-organizer of the National Cancer Policy Forum/Institute of Medicine Workshop on “A Foundation for Evidence-Driven Practice: A Rapid Learning System for Cancer Care”; NIH conference on “Advancing Rare Diseases Research”; and the Congressional Panel on “How I/T is changing Medical Research” (2009). He was a founding board member (2008-2010) of the Executive Review Board of MI3C (Medical Imaging Informatics Innovation Center – a collaboration between IBM and Mayo Clinic). He served as a founding member of the Editorial Board of IEEE Lifesciences Portal. He is a former member of IEEE Multimedia Signal Processing technical committee (2001-2004), an associate editor of IEEE transactions on Multimedia (2002-2005) and a guest editor for the IBM Systems Journal. He was a member of the IBM Academy of Technology (an association of the top IBM technical leaders). He actively represented IBM in Education transformation and healthcare transformation through media interviews and social media presence.