{"id":44501,"date":"2019-04-26T16:14:02","date_gmt":"2019-04-26T15:14:02","guid":{"rendered":"https:\/\/blogs.bmj.com\/bmj\/?p=44501"},"modified":"2019-05-02T16:55:22","modified_gmt":"2019-05-02T15:55:22","slug":"can-ai-fulfill-its-medical-promise","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/bmj\/2019\/04\/26\/can-ai-fulfill-its-medical-promise\/","title":{"rendered":"Can AI fulfil its medical promise?"},"content":{"rendered":"<p class=\"standfirst\">AI technology has challenges to overcome, but it can be a force for good in medicine, say Luxia Zhang, Guilan Kong, Liwei Wang, and Qi-Min Zhan<\/p>\n<p><!--more--><\/p>\n<p><span style=\"font-weight: 400\">The first medical decision support system based on artificial intelligence (AI) was developed in the early 1970s. Although this system was never actually used in medical practice (largely due to the fact that it was a \u201cstandalone\u201d system before the development of personal computers), this pioneering trial pushed a door open to a new world. \u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Over the past decade, with the increasing digitisation of medicine and the progression of technology, applying AI in medicine has become a hot topic. Now it\u2019s largely recognised that through AI we can harness technology to solve medical problems. Obviously, AI cannot replace doctors, but it has great potential to help doctors and patients in many scenarios.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Applying AI in medical imaging and pathology is one of these promising scenarios. <a href=\"https:\/\/www.nature.com\/articles\/s41591-018-0307-0\">Studies have shown<\/a> that the diagnostic accuracy of AI algorithms is comparable to experienced medical experts for diabetic retinopathy, heart disease, and certain cancers.<\/span><span style=\"font-weight: 400\"> An impressive example is detecting pulmonary nodules on lungs in computer tomography scans. It usually takes physicians several minutes to do this, while AI based systems only need a few seconds. Not to mention that machines can work 24\/7, without experiencing the fatigue or diminished judgment that humans do from working long shifts.<\/span><\/p>\n<p><span style=\"font-weight: 400\">AI can support and enhance decision making in more general situations for common diseases. When integrated into clinical decision support systems, AI can provide \u201cevidence based\u201d guidance as to diagnosis and treatment, which is particularly important for countries or regions with substantial variation in healthcare quality. <\/span><span style=\"font-weight: 400\">Those <a href=\"https:\/\/www.nature.com\/articles\/s41591-018-0307-0\">scenarios usually involve less complex algorithms<\/a> and are therefore relatively achievable. <\/span><span style=\"font-weight: 400\">AI can also support <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5070532\/\">doctors\u2019 decision making with complex clinical conditions<\/a>, where it could provide relatively accurate risk prediction, as well as clues for diagnoses and examination suggestions.<\/span><span style=\"font-weight: 400\"> Obviously, though, the caveat here is that these systems are only useful when based on medical literature and real world data that is of a good quality. <\/span><\/p>\n<p><span style=\"font-weight: 400\">In those scenarios, AI functions like a \u201cphysician assistant,\u201d but its end user can also be patients. There have been several commercially available AI powered healthcare products claiming to provide healthcare that is of comparable quality to human doctors, and which are attracting an increasing number of patient users. Furthermore, AI enhanced clinical decision support systems could also be used by health administrations and health insurance sectors, if integrated into medical quality monitoring and improvement processes.<\/span><\/p>\n<p><span style=\"font-weight: 400\">All those scenarios show the benefits of utilising AI in medicine. But one crucial question cannot and should not be ignored: &#8220;Is the information generated by an AI system trustworthy?&#8221; If we need to rely on an AI system to assist our decision making, we should care about its reliability and effectiveness. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Currently, AI algorithms based on deep learning act like a \u201cblack box\u201d: the inner logic behind most machine learning models is hard to explain, and the doctors using them are not given explanations for the advice they receive from these systems. This is not an intuitive way for doctors to practise and raises uncertainty about using AI, since the principle of identifying causality and treating causes is integral to medicine. <\/span><span style=\"font-weight: 400\">Researchers have also expressed concerns about the risks of AI powered diagnostic apps on smartphones providing false reassurance to patients. <a href=\"https:\/\/www.bmj.com\/content\/362\/bmj.k2999\">As the author of a study on skin cancer apps pointed out<\/a>, there\u2019s a possibility that if the app misses red flag symptoms, \u201c<\/span><span style=\"font-weight: 400\">patients will not seek professional advice in the early stages\u201d and miss out<\/span> <span style=\"font-weight: 400\">on early diagnosis.<\/span><\/p>\n<p><span style=\"font-weight: 400\">A century ago, Sir William Osler said that \u201cMedicine is a science of uncertainty and an art of probability.\u201d The concept of \u201cevidence based medicine\u201d seems most apt to deal with the uncertainty that AI\u2019s use in medicine presents. Just like any other new intervention in medicine, the efficacy and safety of AI powered systems should be carefully evaluated before being confidently applied in practice and on patients. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Several <a href=\"https:\/\/www.bmj.com\/company\/newsroom\/bmj-best-practice-and-your-md-enter-strategic-partnership-for-ai-medical-validation\/\">initiatives<\/a> have already applied evidence based medicine principles to verify the medical information generated by AI systems, and hopefully other systems will follow suit.<\/span><span style=\"font-weight: 400\"> We think it\u2019s likely that as technology advances, AI algorithms will become more robust and developed. Yet it\u2019s important that more comparative effectiveness research is carried out to evaluate how good AI algorithms are in a real world setting and how they affect the health outcomes that are important to patients. The performance of predictive AI algorithms also needs to be evaluated in epidemiological or medical research. \u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Finally, we must not forget that the part of medicine that relies on human care and common understanding is still crucial. The knowledge that a doctor collects in consultations and the decision making that follows is a product of reciprocal communication between physicians and patients. Doctors will adapt their questions and advice based on patients&#8217; responses, either verbal or non-verbal, so that they can decide on a treatment path, which reflects a patient\u2019s values and preferences. It\u2019s hard to image how AI could replicate or replace these kinds of interactions. Medicine contains certain \u201chumanity tasks,\u201d such as communication and empathy, that can only be accomplished by humans. \u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">AI technology has challenges to overcome, but it can be a force for good in medicine. If we want to maximise the benefits of \u00a0AI for the sake of patients and the public, then medical doctors, researchers, and AI scientists should work closely together. Using robust methodology, and following ethical norms and no harm principles, we can apply, evaluate, and improve AI technology in medical practice to generate a healthier world.<\/span><\/p>\n<p><em><strong><span class=\"il\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-44509\" src=\"https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Luxia-Zhang.jpg\" alt=\"\" width=\"122\" height=\"133\" \/>Luxia<\/span>\u00a0Zhang<\/strong> is a professor of medicine at Peking University First Hospital, and assistant dean at the <\/em><em style=\"font-size: 1rem\">National Institute of Data Science in Health and Medicine at Peking University.<\/em><em style=\"font-size: 1rem\">\u00a0<\/em><\/p>\n<p><span class=\"il\"><strong>Competing interests:<\/strong> Luxia<\/span>\u00a0Zhang\u00a0received research funding from AstraZeneca.<\/p>\n<p>&nbsp;<\/p>\n<p><em><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-44510\" src=\"https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Guilan-Kong.jpg\" alt=\"\" width=\"122\" height=\"142\" srcset=\"https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Guilan-Kong.jpg 300w, https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Guilan-Kong-257x300.jpg 257w\" sizes=\"auto, (max-width: 122px) 100vw, 122px\" \/>Guilan Kong<\/strong> is an associate research professor at the <em style=\"font-size: 1rem\">National Institute of Data Science in Health and Medicine <\/em>at Peking University.<\/em><\/p>\n<p><strong>Competing interests:<\/strong> None declared.<\/p>\n<p>&nbsp;<\/p>\n<p><em><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-44512\" src=\"https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Liwei-Wang.jpg\" alt=\"\" width=\"122\" height=\"137\" \/>Liwei Wang<\/strong> is a professor in the department of machine intelligence, Peking University, with research interests in machine learning and its applications in medicine.<\/em><\/p>\n<p><strong>Competing interests:<\/strong> None declared.<\/p>\n<p>&nbsp;<\/p>\n<p><em><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-44513\" src=\"https:\/\/blogs.bmj.com\/bmj\/files\/2019\/04\/Qimin-Zhan.jpg\" alt=\"\" width=\"122\" height=\"137\" \/>Qimin Zhan<\/strong> is currently an academician at the Chinese Academy of Engineering, executive vice president of Peking University, president of the Peking University Health Science Center, and dean of the <em style=\"font-size: 1rem\">National Institute of Data Science in Health and Medicine at Peking University<\/em><\/em><\/p>\n<p><strong>Competing interests:<\/strong> None declared.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI technology has challenges to overcome, but it can be a force for good in medicine, say Luxia Zhang, Guilan Kong, Liwei Wang, and Qi-Min Zhan [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/blogs.bmj.com\/bmj\/2019\/04\/26\/can-ai-fulfill-its-medical-promise\/\">More&#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":44504,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2334],"tags":[],"class_list":["post-44501","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-china"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Can AI fulfil its medical promise? 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