Integrating blockchain technology with artificial intelligence for cardiovascular medicine

• Published in: Nature reviews cardiology Vol. 17; no. 1; pp. 1 - 3
• Main Authors: Krittanawong, Chayakrit, Rogers, Albert J, Aydar, Mehmet, Choi, Edward, Johnson, Kipp W, Wang, Zhen, Narayan, Sanjiv M
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.01.2020
• Subjects: Algorithms; Artificial Intelligence; Blockchain; Cardiac arrhythmia; Cardiology; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - physiopathology; Cardiovascular Diseases - therapy; Clinical trials; Data Mining; Datasets; Decision Support Techniques; Diagnosis, Computer-Assisted - methods; Digital currencies; Electrocardiography; Health care policy; Health Information Management - methods; Humans; Medical records; Medical research; Medicine; Patients; Proprietary; Systems Integration; Therapy, Computer-Assisted - methods
• ISSN: 1759-5002; 1759-5010; 1759-5010
• DOI: 10.1038/s41569-019-0294-y

Artificial intelligence (AI) is a rapidly advancing computational discipline that can classify complex data to make accurate predictions. AI has had notable successes in voice, facial and image recognition, in game-playing, in various industrial and scientific fields, and is now being applied to health care1. In cardiovascular medicine, AI can assess cardiac function from imaging2, infer cardiac rhythm and function from the electrocardiogram (ECG)3 and make some clinical decisions as well as experts2. However, a currently unrealized promise of AI is to power personalized cardiovascular solutions by defining novel phenotypes beyond traditional disease syndromes, improving outcome predictions and individualizing therapy. Although AI seems poised to realize this vision of precision medicine, particularly with the rise of wearable sensors and omic technologies, progress has been mixed2. A major bottleneck is the paucity of large, secure, heterogeneous and granular data sets, with accurate follow-up in broad, at-risk populations. This limitation is an increasingly recognized obstacle for AI in cardiovascular medicine4.