Cardiologist and MD Sanjiv Narayan, PhD, is a former neuroscience researcher at the University of California, Los Angeles, where he conducted physiology experiments involving the optical imaging of the brain. He also spent time in San Diego as the co-director of electrophysiology at the University of California San Diego, and director of electrophysiology for the Veterans Affairs Medical Center. Sanjiv M. Narayan is currently a professor of medicine at Stanford University and is interested in improving cardiovascular treatments with technological solutions such as blockchain, neural networks, and machine-learning embedded AI. He recently wrote an article in Nature Reviews in Cardiology (https://www.nature.com/articles/s41569-019-0294-y) discussing how these technologies could be integrated for the care of patients with heart and heart rhythm conditions.
In recent years, artificial intelligence (AI) has been employed in multiple areas of cardiovascular medicine. Examples include the evaluation of cardiac function from imaging results, interpreting electrocardiogram data to cardiac rhythms, and making expert evidence/results-driven clinical decisions.
With the expansion of these applications and more promising future prospects (such as the quest for personalized therapies for novel genetically controlled syndromes which vary among individuals) access to large heterogeneous data sets that inform precision medicine while maintaining patient confidentiality has become an increasingly recognized obstacle to AI advancement. Blockchain technology can serve as a potential solution to this challenge.
Introduced in 2008, Blockchain is a rapidly evolving technological solution that allows secure, traceable, and scalable data exchange. The technology employs a decentralized functionality that eliminates intermediaries from the transaction system, which minimizes security vulnerability to breaches.
The decentralized feature of blockchain can standardize health-data frameworks for AI training and clinical trials as well as regulatory purposes. The technology has the capacity to trace independent record types and also dissect granular data accumulated by AI. This potentially can ensure tracking of clinical data at full capacity, which can serve numerous applications in the area of precision medicine while protecting patient information from bad actors.
Sanjiv Narayan (Abbott) 