When ChatGPT entered the conversation a few years ago, Lloyd B. Minor, a surgeon and the dean of the Stanford School of Medicine, sought to test its ability by asking it about a rare inner ear condition called superior canal dehiscence syndrome. Minor had personally discovered the syndrome, wrote the first paper on it, and developed a surgical procedure to fix it, naturally making him an expert on the subject. The response, he says, was not only concise, accurate, and logical, but as good as anything he could have written. <\/p>\n
\u201cThe way it assimilated the information and presented it was really quite remarkable. And that\u2019s when I realized this is not just an incremental advance. [Large language models] are going to fundamentally change the way we access, learn, and embrace knowledge \u2026 and I think this is a fundamental shift for medicine,\u201d Minor says.<\/p>\n
It\u2019s a sentiment felt widely across the medical field as AI starts to touch nearly every corner of medical research, physician training and processes, and patient care. The rollout of AI across health care is not without challenges, but in some cases, the impact is already being realized.<\/p>\n
LLMs boost medical research<\/p>\n
Perhaps the most visible way AI is impacting the industry is in medical research. Machine learning model AlphaFold2, unveiled by Google<\/a> DeepMind<\/a> in 2020, made it possible to predict the structure of virtually all the 200 million proteins that researchers have identified, opening up avenues to design novel proteins and streamlining the first step in drug research. The model has since been used by more than 2 million people from 190 countries<\/a>, earned its creators the Nobel Prize, and ignited a boom of new biotech startups. Last year, the first AI-targeted and AI-designed drug\u2014an idiopathic pulmonary fibrosis treatment developed by startup Insilico Medicine\u2014reached Phase II clinical trials<\/a>, marking a new milestone for the technology. While no AI-developed drug has made it to patients just yet, the advancements in research processes are already making a material difference.<\/p>\n \u201cRecent advancements in artificial intelligence are revolutionizing active compound discovery by dramatically enhancing the efficiency, accuracy, and scalability previously challenged by traditional methods,\u201d reads a paper<\/a> exploring the impact AI is having on drug discovery, published last month in Nature by a dozen researchers.\u00a0<\/p>\n The research impacts aren\u2019t limited to drug discovery, however. An AI model developed by Cambridge researchers outperformed clinical tests<\/a> in predicting the progression of Alzheimer\u2019s disease, in one example. Another out of Harvard<\/a> was found to accurately detect multiple types of cancers, assess treatments, and predict survival rates. In June, Google DeepMind followed up its line of scientific research models with AlphaGenome<\/a>, a model designed for a wider swath of biology research by predicting how single variants or mutations in human DNA sequences can impact biological processes regulating genes.<\/p>\n Improving the physician and patient experience<\/p>\n The parts of the medical industry being most immediately impacted by AI, according to Wiljeana Glover, a researcher focused on health care innovation and improvement at Babson College\u2019s Kerry Murphy Healey Center for Health Innovation and Entrepreneurship, are the backend processes. For example, large language models are being used in emergency departments to quickly create synopses of complex medical records, and to record and transcribe conversations between providers and patients to generate medical documents<\/a>.\u00a0<\/p>\n \u201cThings that we\u2019re taking off of the clinicians\u2019 plate, that are more administrative, I think those are some of the places where we see AI moving really quickly,\u201d she says.<\/p>\n According to a 2020 study<\/a>, physicians spend 33.4% of their work-related time on non-patient-facing health records and administrative tasks. They then spend an average of two hours per day<\/a> on these tasks outside their official work hours including on weekends, making administrative tasks a leading cause of the burnout plaguing the medical field. Reducing this load not only can give providers more time to focus on direct patient care, but improve the patient experience as well\u2014for example, allowing physicians to make eye contact with patients during visits rather than typing notes.<\/p>\n By improving access to information\u2014and thus treatment\u2014AI can have a notable impact on the physician and patient experience. Large language models allow physicians to more easily query databases containing assimilated knowledge to research potential treatment paths for complex cases. Physicians are also increasingly using AI-powered patient care apps alongside wearables to support ongoing treatment for chronic conditions like hypertension and diabetes, providing more information about the patient\u2019s health outside of in-person visits and increasing communication, Glover says.\u00a0\u00a0<\/p>\n In some cases like radiology, health professionals are now regularly working alongside AI as a routine part of their work<\/a>. Radiologists are using AI to sharpen images, identify abnormalities, and discern subtle changes in areas of concern\u2014essentially using it like a magnifying glass to uncover more information to then evaluate with their expertise. One AI model developed by Northwestern University researchers that can evaluate breast cancer tissue from digital images was even found to perform better at predicting the future course of a patient\u2019s disease than expert pathologists<\/a>, which could help spare patients unnecessary chemotherapy and reduce the duration and intensity of treatments.<\/p>\n A new training paradigm<\/p>\n Even prior to large language models, AI was starting to change what part of physicians\u2019 knowledge base they had to access in their daily work. Minor said that when he was in medical school, they had to memorize medication dosages and indications. Now, they use AI algorithms to optimize dosages based on patient and drug data. A paper in BMC Medical Education<\/a> says AI has come to play \u201ca crucial role in dose optimization and adverse drug event prediction, offering significant benefits in enhancing patient safety and improving treatment outcomes.\u201d<\/p>\n As AI increasingly impacts how physicians work, medical schools are updating how they train doctors<\/a>. George Washington School of Medicine and Health Sciences will be offering a new course this fall on artificial intelligence applications in health care, and the Icahn School of Medicine at Mount Sinai said it will start giving all students access to ChatGPT Edu along with training on how to use it.<\/p>\n The Stanford School of Medicine, Minor says, is in the early stages of reimagining its entire curriculum in light of the advances in generative AI, and the school has formally appointed one of its MD-PhDs in AI to lead its exploration. Minor emphasizes that he\u2019s not suggesting the field move away from requiring a base of medical knowledge, but that learning how to deploy generative AI to augment the ability of physicians to keep up-to-date and bring relevant knowledge to each individual patient needs to be explored educationally.<\/p>\n \u201cWe want to be a pace-setter. We also don\u2019t abandon the essential elements of medical education. But I think defining what those essential elements are should be something we do in a thoughtful way, rather than just assuming that what everyone had to learn in the past, and how they have to learn it, is going to apply moving forward,\u201d he says.<\/p>\n AI is also emerging as a useful training tool<\/a> in its own right. Researchers at New Jersey Institute of Technology, Robert Wood Johnson Medical School, and software company Robust AI developed an AI-powered program that allows students to simulate the basic movements of laparoscopic surgery and provides feedback so they can continuously improve. A recent study from this year showed the program is as good and even slightly better than faculty human evaluators at rating surgical skills.<\/p>\n Far from perfect, but here to stay\u00a0\u00a0<\/p>\n The wide variety of uses for AI within the medical industry isn\u2019t without challenges, drawbacks, and issues that need to be solved.<\/p>\n Even with all the potential for research breakthroughs, drugs being developed with AI still need to be tested in wet labs using physical samples, go through clinical trials, and gain FDA approval. The medical documentation systems being popularized by companies like Augmedix, Abridge<\/a>, and Microsoft-backed Nuance bring the risk of error and hallucination (though it\u2019s worth pointing out that humans\u2014especially ones who are burned out\u2014make errors, too). Algorithms designed to detect illness and injury, and make care decisions, have historically underperformed<\/a> with women and people of color, largely because of these groups\u2019 underrepresentation in the datasets used to train such AI systems. There are also privacy concerns surrounding the training of AI models with patient data<\/a>.\u00a0<\/p>\n \u201cI think we\u2019re moving cautiously, and I think the caution is ideal,\u201d says Glover. \u201cBut absolutely, the way that patients seek, reach, and receive care will be impacted by AI. It\u2019s happening. It\u2019s here.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"When ChatGPT entered the conversation a few years ago, Lloyd B. 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