HONG KONG – South Korea is actively working to expand its artificial intelligence (AI) capabilities in health care, but outdated regulations and concerns over privacy and profit sharing are proving to be significant stumbling blocks.
The convergence of artificial intelligence (AI) and the health care sector was inevitable. The advent of machine-learning and deep-learning technologies capable of analyzing and synthesizing massive amounts of data with algorithms designed to mimic human-level decision-making seems a natural fit for an industry in dire need of greater efficiency.
As a cautious swimmer slips slowly into the cool waters of a giant swimming pool so do biopharma executives: They tread into the murky abyss known as artificial intelligence (AI).
Artificial intelligence (AI) is a technology that takes over when the human brain has reached its limit. For medical technology companies, it could help in the creation of more precise tools and devices that support diagnostic, therapeutic and surgical decisions. Instead of shooting an arrow into the sky, the bullseye target is clear, backed up by volumes of data and a machine learning algorithm designed to pinpoint the biomarker, the abnormality and the probability of success.
Researchers from Johns Hopkins University School of Medicine have developed a machine learning program that could score the risk of pancreatic cysts and recommend one of three treatment strategies – surgery, watchful waiting or discharge without follow-up – more accurately than current methods. The program could potentially reduce the number of unnecessary surgeries performed on pancreatic cysts with little to no potential of turning cancerous.
Some new investors pitched in to Recursion Pharmaceuticals Inc.'s $121 million series C financing, adding to the industry's vision of the importance of AI and machine learning.
TEL AVIV, Israel – Generating all kinds of data that can feed artificial intelligence (AI) and machine learning engines is increasingly cheap and, in many ways, easy but interpreting all that data and translating it into information that is useful to users that range from drug developers to patients remains a significant challenge. Addressing this challenge has blurred the boundaries between traditional technology companies and medical technology companies and forced a rethinking of how treatments are provided, and drugs are developed. And the challenge also creates opportunities for companies that can address this gap.
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