Artificial Intelligence in Medical Software

Ajit K. Pandey, with over two decades of experience in the medical device industry, boasts extensive hands-on expertise in various therapeutic areas. His expertise encompasses artificial intelligence and machine learning in medical software, where he possesses a profound understanding of medical software development, global AI regulations, AI quality systems, and AI software development processes. Ajit holds a B.E. degree from the National Institute of Technology Surat and M.S. degrees from the University of Cincinnati and the University of Southern California. Pramod Gupta has over 25 years of experience as a researcher and academic in various organizations, including collaborations with NASA, GE, VISA, the University of California, and startups. He holds a Ph.D. in Electrical and Computer Engineering from McMaster University, with a specialization in Neuro-Control of Robotic Manipulators. His research areas include Neural Networks, Machine Learning, Artificial Intelligence, Data Modeling and Analytics, and Data Mining.  He has more than 40 publications on these subjects and has co-authored three books published by Springer. Presently, he is an Adjunct Faculty at various universities in the US and works as an independent data science consultant. Naresh K. Sehgal is currently a Cloud Computing Consultant. Before that he worked at Nova Signal for 3 years and at Intel for 31 years in various Engineering and Management roles. Naresh has earned his B.E. from Punjab Engineering College, M.S. and Ph.D. from Syracuse University. He taught a Cloud Computing class at Santa Clara University, where he also earned an MBA. Naresh has 9 patents, co-authored 8 books, published 40 technical papers in various conferences and journals.

• Introduction.- Clinical data management.- Aiml enabled medical device training algorithm selection.- Aiml clinical model training and evaluation.- clinical aiml model transparency.- Clinical aiml model testing and validation.- Aiml  clinical model integration and deployment.- Security and privacy considerations aiml  enabled medical devices.- Medical device machine learning operation mlops.- Medical device risk management human factor and harmonized standards for aiml  enabled medical devices.- AI based health care applications in the cloud case studies.