Researchers Caution Against Sole Reliance on AI in Healthcare, Advocate for Integrated Approach

AI in health care is not a standalone solution, researchers caution

With the advent of artificial intelligence (AI), predictive medicine is becoming an important part of health care, especially in cancer treatment. Predictive medicine uses algorithms and data to help doctors understand how a cancer might continue to grow or react to specific drugs -- making it easier to target precision treatment for individual patients. While AI is important in this work, researchers from University of Maryland School of Medicine (UMSOM) say that it should not be relied on exclusively. Instead, AI should be combined with other methods, such as traditional mathematical modeling, for the best outcomes. In a commentary published April 14 in Nature Biotechnology, Elana Fertig, Ph.D., Director of the Institute for Genome Sciences (IGS) and Professor of Medicine at UMSOM and Daniel Bergman, Ph.D., an IGS scientist argue that mathematical modeling has been underestimated and under-used in precision medicine to date. All health computational models need three key components to work: datasets, equations, and software. Then, after generating data, comes leveraging it to improve early diagnoses, discover new treatments, and aid understanding of the diseases. In a second commentary, out April 15 in Cell Reports Medicine, Dr. Fertig and IGS colleagues Dmitrijs Lvovs, Ph.D., Anup Mahurkar, Ph.D., and Owen White, Ph.D., address how to ethically share health data and methods to create reproducible science. Taken together, the two commentaries set a foundational approach to generating, analyzing, and ethically sharing data to benefit both patients and science. Explaining the argument of the Nature Biotechnology commentary Dr. Fertig said, "AI and mathematical models differ dramatically in how they arrive at an outcome.AI models first must be trained with existing data to make an outcome prediction, while mathematical models are directed to answer a specific question using both data and biological knowledge." That means that when data is sparse -- as it often is in newer cancer treatments such as immunotherapy -- AI can over generalize, resulting in biased or inaccurate outcomes that cannot be reproduced by other scientists. Mathematical modeling, on the other hand, uses known biological mechanisms, learned from scientific experiments, to explain how it arrived at an outcome. "For example, with a mathematical model, we could create virtual cancer cells and healthy cells and write a program that would mimic how those cells interact and evolve inside of a tumor with different types of treatments," said Dr. Bergman, Assistant Professor at IGS and UMSOM's Department of Pharmacology, Physiology, and Drug Development. "At this time, AI cannot give us that type of specificity." The authors state that, in addition to using both types of models in "computational immunotherapy," using a breadth of populations, and making datasets publicly available are critical for the most accurate outcomes. "Data breadth and accuracy are key. Artifacts in the dataset, or even a simple typo in computer code, can throw off the accuracy of either type of model," added Dr. Fertig. "Therefore, for any analysis pipeline to work correctly, it must be reproducible and that can only be assured by open science -- giving access to other researchers whose work can confirm the models will get the right treatment to the right patient." However, reproducibility remains a critical challenge in science. In a 2016 article in Nature surveying more than 1,500 scientists, more than 70% of researchers said they have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. "Reproducible research enables investigators to verify that the findings are accurate, reduce biases, promote scientific integrity, and build trust," explained Dmitrijs Lvovs, Ph.D., Research Associate at IGS and first author on the Cell Reports Medicine commentary. "Because data science is computationally driven, all results should be transparent and automatically reproducible from the same dataset if the analysis code is readily available through open science." While that sounds simple enough -- and there are best practices in place -- the challenge, the authors argue, is how to share data while protecting patient privacy and blocking unauthorized data breaches. Genomic data, when combined with personal health information (PHI), could lead to re-identification of patients, a privacy violation. The authors say that creating ethical open science data sharing means: 1. Getting detailed informed consent from patients; 2. Ensuring data quality when collecting and processing data by mitigating errors; 3. Harmonizing and standardizing data collected from disparate sources; 4. Using and creating resources and platforms, such as multiomic, clinical, public health, and drug discovery repositories; and 5. Working with vetted pipelines, such as open-source analysis tools and software platforms. "Ethical and responsible data sharing democratizes research, supports the advancement of AI, and informs public health policies," said Dr. Lvovs. "With ethical and responsible data sharing, the biomedical research community can maximize the benefits of shared data, accelerate discovery, and improve human health."

While AI Could Be the Game Changer in Predicting Health Outcomes It Should Not Be the Only Method | Newswise

Newswise -- BALTIMORE, April 15, 2025: With the advent of artificial intelligence (AI), predictive medicine is becoming an important part of healthcare, especially in cancer treatment. Predictive medicine uses algorithms and data to help doctors understand how a cancer might continue to grow or react to specific drugs -- making it easier to target precision treatment for individual patients. While AI is important in this work, researchers from University of Maryland School of Medicine (UMSOM) say that it should not be relied on exclusively. Instead, AI should be combined with other methods, such as traditional mathematical modeling, for the best outcomes. In a commentary published April 14 in Nature Biotechnology, Elana Fertig, PhD, Director of the Institute for Genome Sciences (IGS) and Professor of Medicine at UMSOM and Daniel Bergman, PhD, an IGS scientist argue that mathematical modeling has been underestimated and under-used in precision medicine to date. All health computational models need three key components to work: datasets, equations, and software. Then, after generating data comes leveraging it to improve early diagnoses, discover new treatments, and aid understanding of the diseases. In a second commentary, out April 15 in Cell Reports Medicine, Dr. Fertig and IGS colleagues Dmitrijs Lvovs, PhD, Anup Mahurkar, PhD, and Owen White, PhD, address how to ethically share health data and methods to create reproducible science. Taken together, the two commentaries set a foundational approach to generating, analyzing, and ethically sharing data to benefit both patients and science. Explaining the argument of the Nature Biotechnology commentary Dr. Fertig said, "AI and mathematical models differ dramatically in how they arrive at an outcome.AI models first must be trained with existing data to make an outcome prediction, while mathematical models are directed to answer a specific question using both data and biological knowledge." That means that when data is sparse -- as it often is in newer cancer treatments such as immunotherapy -- AI can over generalize, resulting in biased or inaccurate outcomes that cannot be reproduced by other scientists. Mathematical modeling, on the other hand, uses known biological mechanisms, learned from scientific experiments, to explain how it arrived at an outcome. "For example, with a mathematical model, we could create virtual cancer cells and healthy cells and write a program that would mimic how those cells interact and evolve inside of a tumor with different types of treatments," said Dr. Bergman, Assistant Professor at IGS and UMSOM's Department of Pharmacology, Physiology, and Drug Development. "At this time, AI cannot give us that type of specificity." The authors state that in addition to using both types of models in "computational immunotherapy," using a breadth of populations, and making datasets publicly available are critical for the most accurate outcomes. "Data breadth and accuracy are key. Artifacts in the dataset, or even a simple typo in computer code, can throw off the accuracy of either type of model," added Dr. Fertig. "Therefore, for any analysis pipeline to work correctly, it must be reproducible and that can only be assured by open science -- giving access to other researchers whose work can confirm the models will get the right treatment to the right patient." However, reproducibility remains a critical challenge in science. In a 2016 article in Nature surveying more than 1500 scientists, more than 70% of researchers said they have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. "Reproducible research enables investigators to verify that the findings are accurate, reduce biases, promote scientific integrity, and build trust," explained Dmitrijs Lvovs, PhD, Research Associate at IGS and first author on the Cell Reports Medicine commentary. "Because data science is computationally driven, all results should be transparent andautomatically reproducible from the same dataset if the analysis code is readily available through open science." While that sounds simple enough -- and there are best practices in place -- the challenge, the authors argue, is how to share data while protecting patient privacy and blocking unauthorized data breeches. Genomic data, when combined with personal health information (PHI), could lead to re-identification of patients, a privacy violation. The authors say that creating ethical open science data sharing means: 1. Getting detailed informed consent from patients; 2. Ensuring data quality when collecting and processing data by mitigating errors; 3. Harmonizing and standardizing data collected from disparate sources; 4. Using and creating resources and platforms, such as multiomic, clinical, public health, and drug discovery repositories; and 5. Working with vetted pipelines, such as open source analysis tools and software platforms. "Ethical and responsible data sharing democratizes research, supports the advancement of AI, and informs public health policies," said Dr. Lvovs. "With ethical and responsible data sharing, the biomedical research community can maximize the benefits of shared data, accelerate discovery, and improve human health." About the Institute for Genome Sciences The Institute for Genome Sciences' (IGS) has been part of the University of Maryland School of Medicine (UMSOM) since 2007. IGS scientists work in diverse areas, applying genomics and systems biology approaches to better understand health issues in premature infants, women, and transgender people; to improve vaccine development; to study evolutionary biology; and to understand cancer, parasitic, fungal, and infectious diseases, as well as identifying the underpinnings of aging, brain development, addiction, and mental health. IGS also remains at the forefront of high-throughput genomic technologies and bioinformatics analyses through its core facility, Maryland Genomics which provides researchers around the world with cutting-edge, collaborative, and cost-effective sequencing and analysis. About the University of Maryland School of Medicine Now in its third century, the University of Maryland School of Medicine was chartered in 1807 as the first public medical school in the United States. It continues today as one of the fastest growing, top-tier biomedical research enterprises in the world -- with 46 academic departments, centers, institutes, and programs, and a faculty of more than 3,000 physicians, scientists, and allied health professionals, including members of the National Academy of Medicine and the National Academy of Sciences, and a distinguished two-time winner of the Albert E. Lasker Award in Medical Research. With an operating budget of more than $1.2 billion, the School of Medicine works closely in partnership with the University of Maryland Medical Center and Medical System to provide research-intensive, academic, and clinically based care for nearly 2 million patients each year. The School of Medicine has more than $500 million in extramural funding, with most of its academic departments highly ranked among all medical schools in the nation in research funding. As one of the seven professional schools that make up the University of Maryland, Baltimore campus, the School of Medicine has a total population of nearly 9,000 faculty and staff, including 2,500 students, trainees, residents, and fellows. The School of Medicine, which ranks as the 8th highest among public medical schools in research productivity (according to the Association of American Medical Colleges profile) is an innovator in translational medicine, with 606 active patents and 52 start-up companies. In the latest U.S. News & World Report ranking of the Best Medical Schools, published in 2023, the UM School of Medicine is ranked #10 among the 92 public medical schools in the U.S., and in the top 16 percent (#32) of all 192 public and private U.S. medical schools. The School of Medicine works locally, nationally, and globally, with research and treatment facilities in 36 countries around the world. Visit medschool.umaryland.edu