PRI ESPL INT .MELBOURNE TGA1 AI-MEDICAL RESEARCH AI promises to revolutionize medical research, but a cautious approach is needed By Reece Hooker, Production Assistant, 360info Asia-Pacific Melbourne, August 1 (360info) One week, users of social networks are generating and sharing funny images using AI, the next thing we hear, AI can predict the structure of over 200 million proteins. AI is particularly well placed to revolutionize medical research. Technology helps in two vital ways: it streamlines research, and it can make discoveries that humans haven’t made. AI is free from the limitations that come with human researchers: it can sift through deep data sets exponentially faster, never needs to take a break, and never succumbs to illness or fatigue. Taiwanese computer scientist, entrepreneur, and author Kai-Fu Lee recently spoke about his optimism for the future of AI. We have been amazed at how well machine learning algorithms work. It makes us focus on the things AI can’t do, and will likely lead to a greater understanding of the human mystique of how we think, or lead to further advancements, leading to superintelligence. It took 40 years, but I think you’re basically there. None of this means that the rise of machines is imminent. Despite its advances, AI is still hampered by technical limitations. AI pioneer Yosua Bengio said in 2021 that deep learning was nowhere near the intelligence level of a two-year-old today. A study by Pugliese et al. (2021) finds that more and more medical research mentions “machine learning”. But maybe we will have algorithms that are equivalent to lower animals, for perception. And we’re gradually climbing this ladder in terms of tools that allow an entity to explore its environment, he said. Much more work is needed to perfect the technology, and there is no foreseeable date when humans will be superfluous to the world of medical research. There is also the question of ethics in AI, in which policymakers will have to sift through a minefield of questions on issues ranging from data collection to intellectual property and beyond. Meanwhile, leading minds continue to use machine learning and AI to innovate and expand the horizon of what is possible in medical research. REALITY CHECK Between 2000 and 2019, the research community became much more interested in AI: the global output of AI research grew from 52,000 journal publications and conference papers in 2000 to approximately 403,000 in 2019. global economy. Nine out of 10 of 100 healthcare executives surveyed in 2020 said their hospital had an AI and automation strategy. BIG IDEAS “The goal and expectation is that by fully integrating AI into disease development research, many unknowns will be known. What causes diseases to spread, worsen and change, and the additional early signs or symptoms that research has yet to discover, they should reveal themselves with the new frontiers made possible by automated AI,” Hiroaki Kitano, Okinawa Institute of Science and Technology. “The use of AI in medical decision-making is still new, and many barriers must be overcome before it is widely used in clinical practice. For it to reach its full potential, broader research and more rigorous approaches are needed to address the ethical issues it raises. This is an ideal time for medical professionals, stakeholders and governments, as well as individuals and their families, to work together and balance the benefits and risks of new technologies,” Alexander Merkin, Auckland University of Technology. (360info) VM 08011017 NNNN
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