FROM HOPKINSMEDICINE FALL 2023
FUTURE SHOCK Artificial Inteligence is changing medicine by the minute. Our experts weigh in on the promise - and potential perils
Rapid advances in artificial intelligence are changing medicine by the minute. What are the promises? The potential perils? And what lies just around the corner? We surveyed leading AI experts across Johns Hopkins to find out.
By Karen Nitkin, Linell Smith and Sue DePasquale on 10/09/2023 While artificial intelligence technology has been shaping biomedical research and clinical care for years, the release of deep learning models (think: chatbot AI technology) has dramatically accelerated interest in and adoption of AI, particularly in medicine Last spring, The New England Journal of Medicine, citing an explosion in manuscript subscriptions, launched a new “AI in Medicine” series — and announced the 2024 debut of a new journal: NEJM AI.
Meanwhile, leaders at the American Medical Association, noting the promise AI holds “for transforming medicine,” have begun developing recommendations about AI-generated medical advice and content that can be used to advise policymakers and protect patients from misinformation.
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Clearly the AI genie is out of the bottle — and physicians today who neglect or refuse to engage with this latest technology will do so at their own peril, our experts agree. Several Johns Hopkins physicians referred to an oft-cited dictum: “AI will never replace clinicians, but clinicians who don’t use AI will be replaced by those who do.”
GLOSSARY: FREQUENTLY USED TERMS*
Generative AI refers to the capability of an AI system to generate new data or content, such as imag even entire scenarios, in response to user prompts. It involves learning patterns and structures from and using that knowledge to create new examples that are relevant to user prompts and reflect thes structures. It can be used for tasks like generating synthetic medical images, synthesizing patient da simulated scenarios for training purposes. It can also be used to develop general-purpose predictive with flexible inputs and outputs.
Predictive AI focuses on making predictions or forecasts based on available data. It involves building machine learning or other mathematical models that analyze historical data, identify patterns and us terns to make predictions about future events or outcomes. These models can be used for predicting gression, estimating patient outcomes or identifying high-risk individuals who may benefit from early
Black box AI technology refers to the use of complex machine learning models or algorithms whose are not easily interpretable or explainable by users or developers. These models are often referred to es” because the reasoning behind their predictions or decisions is not readily understandable.
Trained on large data sets, they use sophisticated algorithms, such as deep learning neural networks terns and make predictions. While these models can sometimes achieve high accuracy and performa like disease diagnosis, treatment recommendation or patient risk assessment, health care profession difficult to trust and adopt these models if they cannot understand the reasoning behind their recom identify biases or errors that may be embedded in the model’s predictions. *Definitions generated by ChatGPT and reviewed by Tinglong Dai.
FREEING UP TIME
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Freeing clinicians from clerical duties to prevent burnout and increase time with patients is the ideal. But: “We need to be wary of policies where we say, ‘This tool makes you more effective, and so we are going to ask you to do even more. -Mark Dredze
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As electronic medical records begin incorporating AI, Manisha J. Loss, associate chief medical information officer for Johns Hopkins Medicine, envisions a health care future with fewer administrative hassles, better patient experiences and more time for patient-centered care. Already, pilot programs are underway at Johns Hopkins to use AI to draft notes based on clinical conversations, generate responses to patient queries in MyChart and categorize MyChart messages for triage based on clinical versus administrative concerns from patients. But while AI technology can write notes, letters and MyChart responses, it doesn’t discern if the information is accurate or if its message would devastate a recipient. “We need to make sure we have transparency in understanding how these models generate their responses,” Loss says. “And even when we have that transparency, this is clinical care — we still need a clinician to lay eyes on it to make sure it’s appropriate.” Yet in its own way, AI can be remarkably empathetic, able to explain difficult health news without the all-too-human burdens of emotion, exhaustion or distraction. “The patient gets a [written] response that’s thorough and kind and grammatically correct,” says Loss. It’s then up to clinicians to review those computer-generated words and use them as a starting point for their conversation. Computer scientist Mark Dredze and colleagues made headlines last spring with a study, published in JAMA Internal Medicine, that looked at whether ChatGPT can respond accurately to the types of questions patients send to their doctors. Comparing written responses from human physicians and those from ChatGPT, a panel of licensed health care professionals preferred ChatGPT’s answers 79% of the time. The panel perceived ChatGPT’s responses to be more empathetic. While physicians are under punishing time pressure, notes Dredze, “Sometimes all that is needed is to stop and say, ‘I’m sorry you’re going through this.’ It’s such a simple thing, and computers are easily able to remember to do that — especially in writing. Computers have all the time in the world to make empathetic statements.”
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There should be prompt and proper disclosure that the doctor is using AI to write the notes in order to maintain trust with the patient: ‘This was generated by ChatGPT but reviewed by a doctor. - Tinglong Dai
DEMOCRATIZING CARE...AND TRAINING Where the Industrial Revolution enabled the mechanization of human labor, says rheumatologist Antony Rosen, the AI revolution “essentially is enabling the rapid amplification and acceleration of human thinking.” In many ways, AI technology is massively democratizing, he notes, “because everybody now will have access to mental processing powers that used to be the domain of very few.” Preventive care could be improved dramatically with AI by making screening easier and more widely available, says ophthalmologist T.Y. Alvin Liu. Consider diabetic retinopathy, a leading cause of blindness that hits hard in underserved communities. Traditionally, patients would need to make a separate trip to an eye doctor for screening. Starting in 2020, Johns Hopkins deployed IDx-DR, an autonomous AI system, across several Johns Hopkins Community Physicians clinical sites. “Now the screening is not done at an ophthalmologist’s office — it’s done in the primary care setting. The doctor checks your A1C levels, and right then and there, you get your retinal photographs taken. In real time, those photos are analyzed by an AI model that can tell right away whether you have diabetic involvement in the back of the eye,” says Liu. “It’s a one-stop shop. And it has closed the equity gap.” In 2018, pediatric diabetes specialist Risa Wolf led research efforts at Johns Hopkins to use IDx-DR in clinics serving children. “Before we implemented it, about 49% of the kids who needed screening actually had it done. We went to 95% after implementation,” she says. “This AI system has allowed us to improve screening rates for a recommended guideline for kids with diabetes and added convenience and cost savings to families.” Physician Swaroop Vedula and his Johns Hopkins team are developing AI algorithms for systems that analyze surgical performance using data from many sources — such as video, speech, instrument motion, eye gaze and the electronic medical record — and then give feedback to the surgeons and predict patient outcomes. Who will benefit? Surgeons all across the career spectrum, their patients and health care administrators. “If I go to a surgeon one year out of residency, my risk of severe complications after cataract surgery is nine times higher than if I go to a surgeon 10 years into their practice,” says Vedula, citing results from a large Canadian study. “Experience plays a huge role in that variation in outcomes.” Surgery is so highly standardized that it is ideal for AI analysis, he says. “We are going after algorithms that are built on collective intelligence of experiential knowledge and that we can make available to surgeons all over the world.”
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If you want to improve patient care across the world, AI can offe a consistency of excellence. If you are the world’s expert on bone fractures or bone tumors or mam mography, maybe AI will [only] help you a little bit. But the other 95% of care providers become like experts. - Elliot Fishman LIABILITY CONCERNS
Business analytics expert Tinglong D has developed models to better und stand doctors’ decision to use — or n use — AI in their daily practice, partic ly in view of potential malpractice su “Liability concerns have always been instrumental in influencing how phys make decisions. It’s not an exaggerat to say the health care industry has b shaped by the legal industry, and wit coming on the scene, things get real heated,” he says.
One might expect physicians to use technology most often when they ar certain about the optimal treatment for a patient. But in his modeling, Da his colleagues discovered the opposi “Instead of saying, ‘I am going to use to tell me something different,’ our m shows that physicians would use it — fact, overuse it — when they expect i agree with their assessment. This me that AI is not being used to its fullest tential,” he says.
That’s largely due to liability worries: physicians take the step to consult a tool in a case of high uncertainty, the decide to deviate from the AI recom dation, they may be exposed to lega if something goes wrong.
AI demands nothing short of a sea change in the way that medical liabil is defined, Dai says: “We need to brin doctors, lawyers, patient advocates a AI developers together to shape a ne legal environment that supports imp patient care.”
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ACCELERATING DISCOVERY Like many bench scientists, neuroscientist Dwight Bergles says advances in AI are transforming his research by rapidly accelerating the speed of cellular analysis. His lab focuses on the repair of myelin, the insulation around nerve cells that is destroyed in multiple sclerosis, by using microscopy to look into the living brain of a mouse that has been engineered to allow certain cells to fluoresce. “Traditionally,” he says, “we would have to do all of that work by hand, which greatly exceeds what a trainee can do over the period of a Ph.D. or postdoctoral fellowship. It takes months and months and months — sort of a brute force problem — before you can start to get to the interesting parts of the research.” Using AI not only speeds the analysis, he says, but “it dramatically allows us to expand our ability to analyze the entire brain, looking at these more regional differences,” rather than focusing on just one small part of the brain in great detail. Cardiologist Natalia Trayanova and biomedical engineer Sridevi Sarma have both been early leaders in creating and using AI algorithmic tools to improve clinical care. Both cite current concern around the “black box” of AI (see glossary) as a challenge that must be addressed. “The algorithms are there to help physicians make clinical decisions,” says Trayanova. “We want them to be as transparent as possible. The clinicians need to see why the algorithm is making that decision. It’s important for clinical acceptance.” In Sarma’s current work to improve treatment for epilepsy, she has developed a novel computational tool, EZTrack, that analyzes hundreds of electroencephalogram readings to pinpoint the brain’s epileptogenic zone, where seizures originate, to improve the accuracy of surgical removal and surgical outcomes. Crucially, her “systems approach” offers an opportunity for clinicians to peer into the black box to understand how AI reaches its conclusions. She uses imaging data to build a digital brain and then examines connectivity properties of the virtual brain to identify pathological regions. “This is patient-specific, with mathematical equations you can visualize and interpret,” she says. “It only takes a few minutes, and I can hand my deck of slides to the neurosurgeon.” Sarma is currently taking a similar approach to develop AI tools for autism, major depressive disorder, ADHD and frontal temporal dementia.
REAL-WORLD DEPLOYMENT In her research, computer scientist Suchi Saria has collaborated with federal agencies like the FDA to develop new rubrics and tools for improving real-world deployment and evaluation of AI. She is sharing her insights with the National Academy of Medicine, helping to create guidelines for AI use. “When you’re developing a new tool, you take a data set in a lab and test it. That is a fixed, limited data set. It’s controlled. As you move from the lab to the real world, lots of changes can occur. Maybe you’ve trained it one type of population and now you’re deploying it elsewhere. There could be differences in how data is collected or in demographics.” AI needs to account for these changes, she says, while recognizing differences between global, local and even individual patterns. Further, it needs to be transparent, easy to use and responsive to the care team’s input.
DISPELLING DISPARITIES
BREAKING ALL THE RULES
“I hope that it will be a force that will help mitigate some of the disparities,” says Kadija Ferryman. “My fear is that it if it doesn’t, then it’s just another tool that’s part of the same pattern.”
The success of AI is going to requir demics and leaders in AI outside of rosoft, Google and Nvidia, says radi who launched a popular speaker se “Leading Change: Perspectives from 10 years ago.
She gives an example of an algorithm that used accurate data and still made a prediction with discriminatory effects. The algorithm identified Black individuals as needing fewer health care resources even though they were sicker, on average, than white individuals. This is because in the United States, fewer health care dollars are spent on Black people compared with other groups. Instead of sending additional health care resources to sicker people, the algorithm learned from the pattern in the data and repeated it, favoring white individuals. “AI is really good at recognizing patterns,” Ferryman says. “Where it can get tricky is when the data show us patterns that we may not have context for, especially when there are issues of equity and health disparities.” When humans judge surgical skill to assess trainee competency, says Swaroop Vedula, “we run the risk of implicit bias.” He and his Johns Hopkins colleagues, including Shameema Sikder and Vishal Patel, are working on a project “examining whether we find implicit biases in assessing surgical skill and, if so, how do we remove that bias when creating [AI] algorithms to evaluate surgical skill.”
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Like a lot of things in science, people want to move quickly without taking the time to understand what the computer is doing or to properly validate it, to make sure that what it is telling you is actually correct. And the validation step is so important. The danger I see is people will put too much faith in what it does and just accept the results that they’re getting without doing the hard work of going back and making sure that it’s actually correct. - Dwight Bergles
“We have the problems, we have th the solutions. Neither will succeed w Fishman predicts. A harbinger of th New England Journal of Medicine, w years would never publish an article company, recently ran an article on first two authors were from Microso Nuance, which is owned by Microso
“Our patients are reading and heari accuracy, and they are expecting us good news and the challenge,” says entific discoveries start at places lik it takes a bunch of years to get dow nity hospitals. But AI is breaking all a sudden, you have the private prac with AI before the academic institu
ETHICALLY SOURCE AI
As a leader in a National Institutes o sortium (AI-READI) working to gen sets that are “ethically sourced,” Jo mologist T.Y. Alvin Liu and his collea an “AI-ready and equitable atlas for that will serve as a blueprint that ot searchers can follow.
Typically, when technology progres ical aspects have to play catch-up, L AI-READI consortium, “ethical inqui every single stage of the project,” a from subjects from diverse ethnic a backgrounds.”
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LOOMING ISSUES “How will we incorporate AI into the physician workflow? Who will pay for it?” T.Y. Alvin Liu “The first very big barrier is availability of dat to train the algorithms on. There are time and cost barriers to collecting data and properly curating it. We have to strive to avoid biases in data acquisition, which otherwise can result in health care disparities.” Natalia Trayanova “As biologists, we are experiencing a data explosion, where we are greatly exceeding our capacity to analyze the results that we obtained through experiments. There’s sort of this exponential, dramatic increase in the capacity of what we can do.” Dwight Bergles “We’ve created so many checks and balances over decades to ensure patient safety and quality. But that is not aligned with the way that AI models are built. False information can be catastrophic to patient care. Generative AI overall may be a good idea, but we run the risk of destroying the valuable ecosystem e have relied upon to shape how science evolves over time.” Tinglong Dai “There may come a time when machines are going to be really good at doing many tasks more efficiently than humans, and their safety is going to be as good as, or better than, humans’. But what about this in-between phase where you have to figure out if that’s actually true? We need to keep our values as humans, and as physicians, in front of us at all times because those guardrails are going to guide us in how to use these new tools.” Antony Rosen
FUTURE SHOCK
DWIGHT BERGLES Neuroscientist Dwight Bergles is director of the Department of Neuroscience’s imaging center and the Kavli Neuroscience Discovery Institute. He uses AI to visualize connections between nerve cells in the brain to yield insights into how our brains change with aging, injury and disease.
TINGLONG DAI Tinglong Dai, a professor of operations management and business analytics at Johns Hopkins Carey Business School, leads research on human–AI interaction.
SUE DE PASQUALE Consulting editor and former editor of Hopkins Medicine magazine.
MAR Comp tist Ma directo for the kins A whic researc plicatio scienti an
KAREN NITKIN Senior communications specialist for Johns Hopkins Medicine
ANTONY ROSE Rheumatologist Antony Rosen, vice dean for research at the school of medicine, directs inHealth, Johns Hopkins’ program in precision medicine and individualized health, which is fueled by AI advances in data collection and analysis.
SUCHI SARIA Suchi Saria holds the John C. Malone endowed chair and is director of the Machine Learning and Healthcare Lab at Johns Hopkins. She is founder and CEO of Bayesian Health, a company that uses AI to deliver actionable clinical insights, and is on the National Academy of Medicine’s Health Care Artificial Intelligence Code of Conduct steering committee.
SRIDE Biom neer Sr assoc of the Com Medici dictiv prove port fo includ
RK DREDZE puter scienark Dredze is or of research e Johns HopAI-X Foundry, ch drives AI ch and its apons in health, ific discovery nd safety.
KADIJA FERRYMAN Kadija Ferryman, core faculty at the Berman Institute of Bioethics, is an anthropologist who studies race, ethics and policy in health technology, including the ways AI intersects with racial health inequities.
ELLIOT FISHMAN Radiologist Elliot Fishman is director of diagnostic imaging and body computed tomography at Johns Hopkins. He was an early adopter of AI with the Felix Project, an ambitious effort to detect pancreatic tumors early, when they are still resectable.
T.Y. ALVIN LIU T.Y. Alvin Liu directs the Wilmer Precision Ophthalmology Center of Excellence and is a leader in a national consortium, Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI), which is designing a system to ethically collect and generate data.
MANISHA J. LOSS Manisha J. Loss, associate chief medical information officer for Johns Hopkins Medicine and associate professor of dermatology, is working to bring AI technologies to electronic medical records.
EVI SARMA medical engiridevi Sarma, ciate director e Institute of mputational ine, uses preve AI to imclinical supor conditions ding epilepsy.
LINELL SMITH Senior Editor & Writerfor Johns Hopkins Medicine
NATALIA TRAYNOVA Natalia Trayanova, professor of biomedical engineering, is director of research in health and medicine for Johns Hopkins’ universitywide AI-X Foundry and director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation, which aims to develop AI tools for the management of patients with heart rhythm disorders, among other technologies.
SWAROOP VEDULA Physician Swaroop Vedula is on the faculty of the Johns Hopkins Malone Center for Engineering in Healthcare and co-directs the AI for Surgery lab at Johns Hopkins. He specializes in using surgical data science to improve surgical performance analysis, decision-making and outcomes.
RISA WOLF Pediatrician Risa Wolf, director of the Johns Hopkins Pediatric Diabetes Program, was the nation’s first pediatric diabetes specialist to use IDx-DR, approved by the FDA for adults, in studies to improve screening compliance for diabetic retinopathy in children.
Bobby Schnabel
CU Boulder Professor of Computer Science
Robin Dowell
CU Boulder Professor of MCDB
Sriram Sankaranarayanan CU Boulder Professor of Computer Science
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