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. 2. According to Wael Abdel Aal, CEO of telemedicine provider Tele-Med International, healthcare organizations should take advantage of AI to address two … 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. & Tech. Even aside from the variety just mentioned, patients typically see different providers and switch insurance companies, leading to data split in multiple systems and multiple formats. 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. AI can be applied to various types of healthcare data (structured and unstructured). 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. Privacy concerns: When you’re collecting patient data, the privacy of those patients should certainly be a big concern. AI in healthcare has huge and wide reaching potential with everything from mobile coaching solutions to drug discovery falling under the umbrella of what can be achieved with machine learning. Artificial intelligence is here, and it's fundamentally changing medicine. “The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do.”. 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. 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. Another set of risks arise around privacy.5 The requirement of large datasets creates incentives for developers to collect such data from many patients. 6. This fragmentation increases the risk of error, decreases the comprehensiveness of datasets, and increases the expense of gathering data—which also limits the types of entities that can develop effective health-care AI. 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). A current focal point includes re-admission risks, and highlighting patients that have an increased chance of returning to … AI errors are potentially different for at least two reasons. Resource-allocation AI systems could also exacerbate inequality by assigning fewer resources to patients considered less desirable or less profitable by health systems for a variety of problematic reasons. You can opt out anytime. Some patients may be concerned that this collection may violate their privacy, and lawsuits have been filed based on data-sharing between large health systems and AI developers.6 AI could implicate privacy in another way: AI can predict private information about patients even though the algorithm never received that information. Med. Several risks arise from the difficulty of assembling high-quality data in a manner consistent with protecting patient privacy. The findings, interpretations, and conclusions in this report are not influenced by any donation. In fact, those risks are already here. 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. Errors related AI systems would be especially troubling because they can impact so many patients at once. Health-care AI faces risks and challenges. As Price II explained, patients “typically see different providers and switch insurance companies, leading to data split in multiple systems and multiple formats.”. 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.”. “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.”. 3. 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. “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.”, 5. Professional realignment. Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. By signing up you agree to our privacy policy. Successful testing and research have been fueling the interest in AI and robotics applications in surgery. First, we don’t “ship” software anymore, we deploy it instantly. Finally, and least visibly to the public, AI can be used to allocate resources and shape business. Longer-term risks involve shifts in the medical profession. That being said, many healthcare executives are still too shy when it comes to experimenting with AI due to privacy concerns, data integrity concerns or the unfortunate presence of various … Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. The adoption of artificial intelligence in healthcare has been a hot topic and rightly so. One major theme to be addressed in this issue is how to balance the benefits and risks of AI technology. It might even be … Although the field is quite young, AI has the potential to play at least four major roles in the health-care system:1. Rev. Guidance for the Brookings community and the public on our response to the coronavirus (COVID-19) », Learn more from Brookings scholars about the global response to coronavirus (COVID-19) ». A parallel option is direct investment in the creation of high-quality datasets. 61:33 (2019). Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. 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. Pushing boundaries of human performance. 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. 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. Although nurses are trained to double-check, the safety measures reduce the perceived level of risk and the nurses in this study assumed a mistake … (forthcoming 2019), https://papers.ssrn.com/abstract_id=3341692. … A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms.”. Activities supported by its donors reflect this commitment. Microsoft provides support to The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative, and Google provides general, unrestricted support to the Institution. (Indeed, this is often the goal of health-care AI.) “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. AI can have a profound impact, but must meet legal, ethical and regulatory obligations. Ophthalmology and radiology are popular targets, especially because AI image-analysis techniques have long been a focus of development. Artificial Intelligence In Decision Making . 28(8):1042-1047 (2013). 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. It also helped showcase how we’re only just beginning to glimpse the potential of AI, and there are still plenty of concerns around its abilities. 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. “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. Clinical laboratories working with AI should be aware of ethical challenges being pointed out by industry experts and legal authorities. The nirvana fallacy. 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. A. Michael Froomkin et al., When AIs Outperform Doctors: The Dangers of a Tort-Induced Over-Reliance on Machine Learning, 61 Ariz. L. Rev. 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. Data availability. Quality oversight. 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. Reflecting this direction, both the United States’ All of Us initiative and the U.K.’s BioBank aim to collect comprehensive health-care data on huge numbers of individuals. Read how it has affected things like personalized care, and see what a critic has to say. Of course, many injuries occur due to me… Several programs use images of the human eye to give diagnoses that otherwise would require an ophthalmologist. 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. AI machines use machine learning algorithms to mimic the cognitive abilities of human beings and solve a simple or complex problem. Data availability: The logistics related to the patient data needed to develop a legitimate AI algorithm can be daunting. Provider engagement and education. 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. Health Pol’y L. & Ethics (forthcoming 2019), 21 Yale J.L. Could this phenomenon occur and lead to inaction in the American healthcare system? Managing patients and medical resources. Even a massive company such as Google can experience problems related to patient data and privacy, showing that it’s something everyone involved in AI must take seriously. AI programmed to do something dangerous, as is the case with autonomous weapons programmed to kill, is one way AI can pose risks. The agency has already cleared several products for market entry, and it is thinking creatively about how best to oversee AI systems in health. W. Nicholson Price II & I. Glenn Cohen, Privacy in the age of medical big data, Nature Medicine 25:37-43 (2019). When talking about the potential risks of healthcare AI, one speaker made an unsettling comparison between the technology and a certain dangerous mineral. Safety measures implemented during drug dispensing involve multiple cross-checks by different colleagues before a drug is given to a patient. 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. 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. AI has the potential for tremendous good in health care. 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. 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. Fortunately, there is a change we can believe in. But the current system is also rife with problems. Nenad Tomašev et al., A clinically applicable approach to continuous prediction of future acute kidney injury, Nature 572: 116-119 (2019). 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. 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. W. Nicholson Price II, Regulating black-box medicine, Mich. L. Rev. At the heart of many innovations in healthcare are patients and finding ways to improve the quality of their care and experience. 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. The free newsletter covering the top headlines in AI. Ensuring effective privacy safeguards for these large-scale datasets will likely be essential to ensuring patient trust and participation. Thus, complex operations are conducted with minimal pain, blood loss, and low risks of side effects. 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. The Food and Drug Administration (FDA) oversees some health-care AI products that are commercially marketed. 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. A recent study published in Nature (in collaboration with Google) reports that Google AI detects breast cancer better than human doctors. AI in Healthcare – Benefits, Challenges & Risks Artificial Intelligence (AI) has the potential to have a transformative impact on the healthcare industry. Some medical specialties, such as radiology, are likely to shift substantially as much of their work becomes automatable. The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do. Privacy concerns. 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. Laura M. Cascella, MA. AI can also share the expertise and performance of specialists to supplement providers who might otherwise lack that expertise. “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.). Data are typically fragmented across many different systems. Democratizing medical knowledge and excellence. Artificial Intelligence has played a major role in decision making. While AI offers a number of possible benefits, there also are several risks: Injuries and error. Experts are voicing concerns that using artificial intelligence (AI) in healthcare could present ethical challenges that need to be addressed. AI in healthcare also presents various risks related to patient safety, discrimination bias, fraud and abuse, cybersecurity, among others. Automating drudgery in medical practice. 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. According to a, (More AI in Healthcare coverage of this specific risk can be read. A guide to healthy skepticism of artificial intelligence and coronavirus, Artificial Intelligence and Emerging Technology (AIET) Initiative. Risk in clinical practice is often obfuscated by the complexities of the science. 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. Healthcare providers are already using various types of artificial intelligence, such as predictive analytics or machine learning, to address various issues. These health-care AI systems fall into something of an oversight gap. Of course, many injuries occur due to medical error in the health-care system today, even without the involvement of AI. The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other health-care problems may result. 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. 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. 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. One final risk bears mention. 116(3):421-474 (2017). There are risks involving bias and inequality in health-care AI. 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. The nirvana fallacy posits that problems arise when policymakers and others compare a new option to perfection, rather than the status quo. 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. In either case—or in any option in-between—medical education will need to prepare providers to evaluate and interpret the AI systems they will encounter in the evolving health-care environment. As smart systems become … One set of potential solutions turns on government provision of infrastructural resources for data, ranging from setting standards for electronic health records to directly providing technical support for high-quality data-gathering efforts in health systems that otherwise lack those resources. 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. Bias and inequality. 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. AI can automate some of the computer tasks that take up much of medical practice today. 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. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars. Artificial Intelligence Risks: Patient Expectations . Joan Palmiter Bajorek, Voice recognition still has significant race and gender biases, Harvard Bus. But though the benefits and applications are manifest, AI comes with a number of challenges and risks that will need to be addressed if … 4. 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. Training AI systems requires large amounts of data from sources such as electronic health records, pharmacy records, insurance claims records, or consumer-generated information like fitness trackers or purchasing history. AI systems learn from the data on which they are trained, and they can incorporate biases from those data. AI innovation has already demonstrated significant promise in healthcare by reducing costs to providers and improving quality and access to patients.. Accenture predicts the healthcare AI market to be worth $6.6 billion by 2021 and experience a 40% CAGR. Post was not sent - check your email addresses! Doing nothing because AI is imperfect creates the risk of perpetuating a problematic status quo. Patient Risk Identification - By analysing vast amounts of historic patient data, AI solutions can provide real-time support to clinicians to help identify at risk patients. First, shipping. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Professional realignment: One long-term risk of implementing AI technology is that it could lead to “shifts in the medical profession.”, “Some medical specialties, such as radiology, are likely to shift substantially as much of their work becomes automatable,” Price II wrote. There are several ways we can deal with possible risks of health-care AI: Data generation and availability. But health data are often problematic. Oversight of AI-system quality will help address the risk of patient injury. (May 10, 2019), https://hbr.org/2019/05/voice-recognition-still-has-significant-race-and-gender-biases. Even just gathering all of the necessary data for a single patient can present various challenges. Researchers may work to ensure that patient data remains private, but there are always malicious hackers waiting in the wings to exploit mistakes. 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. 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. Guide to healthy skepticism of artificial intelligence ( AI ) in healthcare could ethical... 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