While regulatory science can lag behind technology advances, the FDA has for the past few years been exploring ways to harness the potential of artificial intelligence (AI) to streamline drug development and the approval process.
Recognizing that the age of artificial intelligence (AI) has arrived and, with it, the potential to transform everything from health care to transportation and manufacturing, U.S. President Donald Trump issued an executive order earlier this year that launched the American AI Initiative.
BEIJING – With home-grown artificial intelligence (AI) medical devices under priority review, mainland China is quickly putting together a regulatory framework to more rapidly tap into the power of AI to develop devices and drugs.
HONG KONG – With an aging population and a shortage of doctors, Japan is now working to develop artificial intelligence (AI)-based medicine faster than any other country in Asia.
PERTH, Australia – It's likely that Australia will not draft separate guidance or regulations for software applications that use artificial intelligence or machine learning (AI/ML) for drug development or medical devices.
HONG KONG – South Korea is actively working to expand its artificial intelligence (AI) capabilities in health care, but outdated regulations and concerns over privacy and profit sharing are proving to be significant stumbling blocks.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.
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