One week social-media users are generating and sharing amusing images using AI, the next we hear AI can predict the structure of over 200 million proteins.
AI is particularly well placed to revolutionise medical research. The technology helps in two vital ways: it optimises research, and it can make discoveries that humans have not. AI is unshackled from the limitations that come with human researchers: it can trawl deep datasets exponentially faster, never needs to take a break, and never succumbs to illness or fatigue.
Taiwanese computer scientist, businessman and author Kai-Fu Lee recently spoke about his optimism for the future of AI.
“We’ve surprised ourselves with how well machine learning algorithms work. … It makes us focus on the things that AI cannot do, and it will probably either lead to a greater understanding of the human mystique of how we think or it will lead to more breakthroughs, leading to superintelligence … It took 40 years, but I think we’re basically there.”
None of this means the rise of the machines is imminent. Despite its advances, AI is still encumbered by technical limitations. AI pioneer Yosua Bengio said in 2021 that deep learning was not “anywhere close today to the level of intelligence of a two-year-old child”.
“But maybe we 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 needs to be done to perfect the technology, and there is no foreseeable date by which humans will be superfluous to the world of medical research. There is also the matter of ethics in AI, in which legislators will have to parse a minefield of questions about issues ranging from data collection to intellectual property and beyond.
In the meantime, leading minds continue to use machine learning and AI to innovate and expand the horizon of what’s 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 roughly 403,000 in 2019. AI is projected to contribute US$15.7 trillion to the 2030 global economy. Nine in 10 of the 100 healthcare executives surveyed in 2020 said their hospital had an AI and automation strategy in place.
BIG IDEAS
Quote attributable to Hiroaki Kitano, Okinawa Institute of Science and Technology:
"The goal and expectation is that in fully integrating AI into research about disease development, many unknowns will become known. What causes diseases to spread, worsen and change, and additional early signs or symptoms that research has yet to uncover, should reveal themselves with the new frontiers made possible by automated AI."
Quote attributable to Alexander Merkin, Auckland University of Technology:
"The use of AI in making medical decisions is still new and many barriers need to be overcome before it is used widely in clinical practice. For it to reach its full capacity, wider research and more rigorous approaches are needed to grapple with 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 seek a balance between the benefits and risks of the new technologies."
PERSPECTIVES
AIs could predict dementia, humans can ensure it’s done ethically Alexander Merkin, Auckland University of Technology AI could mean early intervention for dementia but raises ethical issues about patient privacy.
Is there an AI in the house? Hiroaki Kitano, Okinawa Institute of Science and Technology The quest to discover how diseases develop in humans has been hamstrung by high costs and access to large enough datasets. Scientists are racing to change that.
Waking up to AI's sleep loss potential Simon Angus, Monash University Everyone sleeps, but we have few tools for measuring the sleep the world is getting at scale. AI and sleep could help us study global shocks in near-real time.
When an AI can call code blue Sherif Gonem, University of Nottingham Artificial intelligence can help spot the early signs of deteriorating patients in hospital wards, but its application is yet to be tested.
AI a jolt of energy for Indonesian medical research Akhmad Musafa, Universitas Budi Luhun, Ardyono Priyadi and Mauridhi Hery Purnomo - Institut Teknologi Sepuluh Nopember Indonesia’s growth as a medical research hub will only go as far as its capacity to power its public health facilities. AI could speed up the process.
AI in the pharmaceutical industry promises cheaper, faster, better drugs Feby Artwodini Muqtadiroh, Mauridhi Hery Purnomo, and I Ketut Eddy Purnama, Institut Teknologi Sepuluh Nopember Finding potential new drugs is becoming faster and cheaper, thanks to artificial intelligence, but challenges remain.
Machine learning could help predict future dengue fever outbreaks Wiwik Anggraeni and Mauridhi Hery Purnomo, Institut Teknologi Sepuluh Nopember Indonesia’s dengue fever outbreaks are increasingly frequent. Artificial Intelligence can help to predict where the next hotspot will be.
AI helps science keep up in COVID-19 mutation race Berlian Al Kindhi, and Mauridhi Hery Purnomo, Institut Teknologi Sepuluh Nopember, Surabaya Artificial intelligence can help predict mutations in COVID-19. Some have achieved a perfect strike rate: 100 percent correct.
AI analysis shows its worth in making sense of online health Safitri Juanita, Universitas Budi Luhur, and Mauridhi Heri Purnomo and Diana Purwitasari, Institut Teknologi Sepuluh Nopember Natural language processing is an emerging, complex form of analysis. But a study from Indonesia showed that the computers can draw sense from doctors’ chats.
Originally published under Creative Commons by 360info™.
