Oversight of AI-system quality will help address the risk of patient injury. Finally, and least visibly to the public, AI can be used to allocate resources and shape business. AI systems learn from the data on which they are trained, and they can incorporate biases from those data. If an AI system recommends the wrong drug for a patient, fails to notice a tumor on a radiological scan, or allocates a hospital bed to one patient over another because it predicted wrongly which patient would benefit more, the patient could be injured. But the current system is also rife with problems. Several risks arise from the difficulty of assembling high-quality data in a manner consistent with protecting patient privacy. Doing nothing because AI is imperfect creates the risk of perpetuating a problematic status quo. AI errors are potentially different for at least two reasons. A hopeful vision is that providers will be enabled to provide more-personalized and better care, freed to spend more time interacting with patients as humans.11 A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms. Report Produced by Center for Technology Innovation. Injuries and error: “The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other healthcare problems may result,” author W. Nicholson Price II, University of Michigan Law School, wrote. How it's using AI in healthcare: Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. “AI could implicate privacy in another way: AI can predict private information about patients even though the algorithm never received that information,” Price II added. Sorry, your blog cannot share posts by email. While AI offers a number of possible benefits, there also are several risks: Injuries and error.The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other health-care problems may result. (forthcoming 2019), https://papers.ssrn.com/abstract_id=3341692. W. Nicholson Price II, Artificial intelligence in the medical system: four roles for potential transformation, 18 Yale J. There are risks involving bias and inequality in health-care AI. Patient care may not be 100% perfect after the implementation of AI, in other words, but that doesn’t mean things should remain the same as they’ve always been. Artificial Intelligence has played a major role in decision making. Activities supported by its donors reflect this commitment. One major theme to be addressed in this issue is how to balance the benefits and risks of AI technology. The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do. Pushing boundaries of human performance. Nenad Tomašev et al., A clinically applicable approach to continuous prediction of future acute kidney injury, Nature 572: 116-119 (2019). AI can also share the expertise and performance of specialists to supplement providers who might otherwise lack that expertise. Longer-term risks involve shifts in the medical profession. Second, if AI systems become widespread, an underlying problem in one AI system might result in injuries to thousands of patients—rather than the limited number of patients injured by any single provider’s error. Monika K. Goyal et al., Racial disparities in pain management of children with appendicitis in emergency departments, JAMA Pediatrics 169(11):996-1002 (2015). Risk in clinical practice is often obfuscated by the complexities of the science. And in this modern era of online patient reviews, it would not take long for the word to get out that a providers’ AI capabilities could not be trusted. Successful testing and research have been fueling the interest in AI and robotics applications in surgery. “The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do.”. Of course, many injuries occur due to me… Managing patients and medical resources. “Some scholars are concerned that the widespread use of AI will result in decreased human knowledge and capacity over time, such that providers lose the ability to catch and correct AI errors and further to develop medical knowledge.”, (More AI in Healthcare coverage of this specific risk can be read here, here and here.). Health Pol’y L. & Ethics (forthcoming 2019), 21 Yale J.L. The adoption of artificial intelligence in healthcare has been a hot topic and rightly so. Using these programs, general practitioner, technician, or even a patient can reach that conclusion.3 Such democratization matters because specialists, especially highly skilled experts, are relatively rare compared to need in many areas. Rev. 6. As with all things AI, these healthcare technology advancements are based on data humans provide – meaning, there is a risk of data sets containing unconscious bias. February 14, 2020 - Artificial Intelligence (AI) adoption is gradually becoming more prominent in health systems, but 75 percent of healthcare insiders are concerned that AI could threaten the security and privacy of patient data, according to a recent survey from KPMG. Data availability: The logistics related to the patient data needed to develop a legitimate AI algorithm can be daunting. For instance, Google Health has developed a program that can predict the onset of acute kidney injury up to two days before the injury occurs; compare that to current medical practice, where the injury often isn’t noticed until after it happens.2 Such algorithms can improve care beyond the current boundaries of human performance. Joan Palmiter Bajorek, Voice recognition still has significant race and gender biases, Harvard Bus. The healthcare industry, in its continuing efforts to drive down costs and improve quality, will increasingly seek to leverage AI when rendering medical services and seeking reimbursement for such services. For example, African-American patients receive, on average, less treatment for pain than white patients;8 an AI system learning from health-system records might learn to suggest lower doses of painkillers to African-American patients even though that decision reflects systemic bias, not biological reality. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. (Indeed, this is often the goal of health-care AI.) Although the field is quite young, AI has the potential to play at least four major roles in the health-care system:1. AI has the potential for tremendous good in health care. Despite its potential to unlock new insights and streamline the way providers and patients interact with healthcare data, AI may bring not inconsiderable threats of privacy problems, ethics concerns, and medical errors. Provider engagement and education. Forward-thinking minds like Stephen Hawking and Elon Musk have all warned about the consequences of AI, and it’s worth wondering about its imminent application in an industry as crucial to human survival as health care. Bias and inequality: If the data used to train an AI system contains even the faintest hint of bias, according to the report, that bias will be present in the actual AI. Few doubt too that while AI in healthcare promises great benefits to patients, it equally presents risks to patient safety, health equity and data security. This report from The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “AI Governance,” a series that identifies key governance and norm issues related to AI and proposes policy remedies to address the complex challenges associated with emerging technologies. AI can automate some of the computer tasks that take up much of medical practice today. As smart systems become … Providers spend a tremendous amount of time dealing with electronic medical records, reading screens, and typing on keyboards, even in the exam room.4 If AI systems can queue up the most relevant information in patient records and then distill recordings of appointments and conversations down into structured data, they could save substantial time for providers and might increase the amount of facetime between providers and patients and the quality of the medical encounter for both. For instance, AI systems might predict which departments are likely to need additional short-term staffing, suggest which of two patients might benefit most from scarce medical resources, or, more controversially, identify revenue-maximizing practices. 6 serious risks associated with AI in healthcare, The rapid rise of AI could potentially change healthcare forever, leading to faster diagnoses and allowing providers to spend more time communicating directly with patients. Notes from Internet Governance Forum (IGF) 2020 on the use of AI in healthcare, and how we could respond to them. Patients might consider this a violation of their privacy, especially if the AI system’s inference were available to third parties, such as banks or life insurance companies. READ MORE: Top 4 Ways to Advance Artificial Intelligence in Medical Imaging “I think of machine learning kind of as asbestos,” said Jonathan Zittrain, a professor at Harvard Law School, per STAT News. The only reasonable way to ensure that the benefits are maximised and the risks are minimised is if doctors and those from across the wider health and care landscape take an active role in the development of this technology today. Second, the Affordable Care Act creates the ability for startups to own risk end-to-end: full-stack startups for healthcare. Risk for Doctors & Patients AI can also pose a risk for doctors and patients. At the heart of many innovations in healthcare are patients and finding ways to improve the quality of their care and experience. The agency has already cleared several products for market entry, and it is thinking creatively about how best to oversee AI systems in health. Increased oversight efforts by health systems and hospitals, professional organizations like the American College of Radiology and the American Medical Association, or insurers may be necessary to ensure quality of systems that fall outside the FDA’s exercise of regulatory authority.10, “A hopeful vision is that providers will be enabled to provide more-personalized and better care. … A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms.”. Artificial intelligence is here, and it's fundamentally changing medicine. Post was not sent - check your email addresses! The free newsletter covering the top headlines in AI. Errors related AI systems would be especially troubling because they can impact so many patients at once. By signing up you agree to our privacy policy. Data availability. 116(3):421-474 (2017). Legal and ethical risks of AI in healthcare September 2, 2020 TORONTO – With the onset of a global pandemic, the imperative to innovate in the healthcare sector is even more pressing. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more. A parallel option is direct investment in the creation of high-quality datasets. In addition, patients and the patients’ family and friends are likely to not react well if they find out “a computer” is the reason a significant mistake was made. The nirvana fallacy: The nirvana fallacy, Price II explained, occurs when a new option is compared to an ideal scenario instead of what came before it. Data are typically fragmented across many different systems. Experts are voicing concerns that using artificial intelligence (AI) in healthcare could present ethical challenges that need to be addressed. But though the benefits and applications are manifest, AI comes with a number of challenges and risks that will need to be addressed if … Artificial Intelligence In Decision Making . Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare providers for shifting roles in an evolving system. While AI offers a number of possible benefits, there also are several risks: Injuries and error. Lauren Block et al., In the wake of the 2003 and 2011 duty hours regulations, how do internal medicine interns spend their time?, J. Gen. Intern. A. Michael Froomkin et al., When AIs Outperform Doctors: The Dangers of a Tort-Induced Over-Reliance on Machine Learning, 61 Ariz. L. Rev. When talking about the potential risks of healthcare AI, one speaker made an unsettling comparison between the technology and a certain dangerous mineral. Similarly, if speech-recognition AI systems are used to transcribe encounter notes, such AI may perform worse when the provider is of a race or gender underrepresented in training data.7, “Even if AI systems learn from accurate, representative data, there can still be problems if that information reflects underlying biases and inequalities in the health system.”. For instance, an AI system might be able to identify that a person has Parkinson’s disease based on the trembling of a computer mouse, even if the person had never revealed that information to anyone else (or did not know). AI in Healthcare – Benefits, Challenges & Risks Artificial Intelligence (AI) has the potential to have a transformative impact on the healthcare industry. With such revolutions in the field of healthcare, it is clear that despite the risks and the so-called ‘threats’, Artificial Intelligence is benefiting us in many ways. There are several ways we can deal with possible risks of health-care AI: Data generation and availability. AI can have a profound impact, but must meet legal, ethical and regulatory obligations. However, many AI systems in health care will not fall under FDA’s purview, either because they do not perform medical functions (in the case of back-end business or resource-allocation AI) or because they are developed and deployed in-house at health systems themselves—a category of products FDA typically does not oversee. AI Surgical System allows for performing the tiniest and the most accurate movements. “For instance, if the data available for AI are principally gathered in academic medical centers, the resulting AI systems will know less about—and therefore will treat less effectively—patients from populations that do not typically frequent academic medical centers,” Price II wrote. 61:33 (2019). AI in healthcare also presents various risks related to patient safety, discrimination bias, fraud and abuse, cybersecurity, among others. According to Wael Abdel Aal, CEO of telemedicine provider Tele-Med International, healthcare organizations should take advantage of AI to address two … It might even be … The nirvana fallacy posits that problems arise when policymakers and others compare a new option to perfection, rather than the status quo. AI programmed to do something dangerous, as is the case with autonomous weapons programmed to kill, is one way AI can pose risks. W. Nicholson Price II & I. Glenn Cohen, Privacy in the age of medical big data, Nature Medicine 25:37-43 (2019). W. Nicholson Price II, Regulating black-box medicine, Mich. L. Rev. There is benefit to swiftly integrating AI technology into the health care system, as AI poses the opportunity to improve the efficiency of health care delivery and quality of patient care. A current focal point includes re-admission risks, and highlighting patients that have an increased chance of returning to … Med. Of course, many injuries occur due to medical error in the health-care system today, even without the involvement of AI. Ensuring effective privacy safeguards for these large-scale datasets will likely be essential to ensuring patient trust and participation. Fortunately, there is a change we can believe in. Read how it has affected things like personalized care, and see what a critic has to say. These are six potential risks of AI that were identified in the nonprofit organization’s report: 1. Quality oversight. The Food and Drug Administration (FDA) oversees some health-care AI products that are commercially marketed. But evidence of risk homeostasis between clinicians has been found, for example, in a recent study of nurses in an Intensive Care Unit in the UK. Some scholars are concerned that the widespread use of AI will result in decreased human knowledge and capacity over time, such that providers lose the ability to catch and correct AI errors and further to develop medical knowledge.9. A guide to healthy skepticism of artificial intelligence and coronavirus, Artificial Intelligence and Emerging Technology (AIET) Initiative. I. Glenn Cohen & Michelle M. Mello, Big data, big tech, and protecting patient privacy, JAMA (published online Aug. 9, 2019), https://jamanetwork.com/journals/jama/fullarticle/2748399. Even just gathering all of the necessary data for a single patient can present various challenges. First, patients and providers may react differently to injuries resulting from software than from human error. The company’s automated platform prioritizes patient illness/injury, tracks hospital waiting times and can even chart the fastest ambulance routes. The nirvana fallacy. According to a, (More AI in Healthcare coverage of this specific risk can be read. Privacy concerns: When you’re collecting patient data, the privacy of those patients should certainly be a big concern. The study, published in the medical journal BMJ, notes the increasing concerns surrounding the ethical and medico-legal impact of the use of AI in healthcare and raises some important clinical safety questions that should be considered to ensure success when using these technologies. The rapid rise of AI could potentially change healthcare forever, leading to faster diagnoses and allowing providers to spend more time communicating directly with patients. Something of an oversight gap role in decision making from competing algorithms. ” improve the of. Working with AI should be aware of ethical challenges being pointed out by industry experts and authorities! 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