AI in Healthcare and Life Sciences: Further Adoption Requires Better Data Infrastructure

The following is a guest article by Jon Kimerle, Global Strategic Healthcare Alliances at Pure Storage  The digital transformation of healthcare continues to accelerate, and technologies like artificial intelligence (AI) are predicted to have an increasing impact on efficiency, quality, and scalability of health outcomes.  At present, AI is already driving high-quality, predictive patient care and better outcomes by making connections humans can’t, adding context, and unearthing new insights. The data generated by AI can facilitate early disease detection, personalized medications and care, the prevention of diagnostic or prescription interaction errors, analysis of treatment risks, and much more. Clinical AI algorithms have already begun to catalyze progress and promise in fields such as image-based diagnosis in dermatology and radiology, patient monitoring and management, and genome interpretation and drug discovery.  While adoption of AI applications in healthcare is low, a recent study found that more than half of IT buyers are prioritizing AI/Machine Learning technology investments for the next five years, meaning there is a unique opportunity for healthcare and life science organizations to leverage AI for faster, safer, and more impactful outcomes. To succeed, however, organizations need to address critical issues slowing adoption and limiting impact: limitations on data storage, data access issues, and data security and protection. Rethinking Data Storage and Acc...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Analytics/Big Data C-Suite Leadership Health IT Company Healthcare IT Hospital - Health System IT Infrastructure and Dev Ops Security and Privacy Artificial Intelligence Clinical AI Algorithms Cyber Attacks Data A Source Type: blogs