Vinitha Subbhuraam

Dr. Vinitha Subbhuraam is a visionary leader in AI and digital transformation, with over 20 years of experience delivering high-impact AI/ML solutions and driving measurable business growth across various technology sectors. She is adept at leading go-to-market strategies, forging strategic partnerships with Fortune 500 companies and innovation hubs.

She is currently the Founder of AI First Innovations, a consultancy firm specializing in translating complex real-world challenges into innovative, revenue-generating solutions leveraging emerging technologies, including Generative and Agentic AI, to solve pressing business problems.

She is also the Founder of IMPACT Labs Academy, an EdTech venture that empowers the next generation with AI skills, critical thinking & innovation skills, as well as a sense of purpose, through a learner-centered model customized for the age of AI.

She has a strong academic and research presence and has published over 100 peer-reviewed journal articles and two books on disruptive solutions for highly complex problems and the next big ideas directly impacting global healthcare. She is also the co-Editor-in-Chief for a leading Q1 journal in computer science – Computer Methods and Programs in Biomedicine. She has been consistently listed among the top computer science researchers worldwide in the Research.com Global Rankings.

Selected Publications

Automated risk prediction of post-stroke adverse mental outcomes using deep learning

Deep learning applied to sequential clinical data predicts adverse psychological outcomes after stroke, enabling earlier intervention by integrating medical and behavioral health data.

View on PubMed →

An introduction to the Cyrcadia Breast Monitor

A non-invasive, non-radiogenic wearable that records thermodynamic metabolic data from breast tissue, offering a new approach to early cancer detection and personalized monitoring.

View on ScienceDirect →

Nondimensional diabetes indices for accurate diagnosis of diabetic subjects

Indices derived from modeling the glucose-insulin regulatory system distinguish normal, at-risk, and diabetic subjects — extended by CGMDI toward real-time, continuous diagnostics.

View on PubMed →

Link to all publications: Google Scholar →

Invited Articles

Beyond Bias: Leveraging AI to Improve Cardiovascular Outcomes in Women

A compelling case on how AI can dismantle decades of systemic inequality in women's CVD — across awareness, risk prediction, diagnostics, and treatment.

Read on mDisrupt →

Books

Predictive analytics (PA) is a branch of advanced analytics that uses techniques such as statistics, modeling, data mining, and artificial intelligence to analyze past and current real-time data to gain fast and accurate insights into several aspects of healthcare. Several books, publications, and courses online and offline provide comprehensive coverage of the technical implementation side of AI. However, few books present the application-oriented side of using PA and artificial intelligence (AI) in healthcare. The applications of AI in healthcare are evolving at an unprecedented pace, reshaping how we approach disease prevention, diagnosis, treatment, and medical education. Volume 1 of this book introduces PA and AI in layman's terms so that the information is accessible and digestible to providers, patients, and medical students who are now recognizing this paradigm shift in healthcare and becoming active participants. Volume 1 presents applications and case studies in public health surveillance, women's health, telemedicine, and pervasive healthcare. Volume 2 dives deeper into cutting-edge advancements and more real-world applications of AI, including academic entrepreneurship, generative AI in medical education, digital transformation in complementary and alternative medicine, men's health, and precision medicine.