Author Interviews / 11.07.2025
The Use of AI in Literature Review and Meta-Analysis in Medical Research
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Medical research is at the heart of clinical advancement. Whether evaluating the safety of new treatments or analyzing trends across patient populations, the integrity and efficiency of research practices have direct implications on healthcare delivery. Among the most labor-intensive tasks in medical research are literature reviews and meta-analyses—two foundational methodologies that aggregate findings from multiple studies to draw broader, evidence-based conclusions.
With the volume of published medical literature increasing exponentially each year, traditional methods of reviewing research have become less sustainable. Today, artificial intelligence (AI) is beginning to play a transformative role in this process, offering ways to streamline literature searches, extract relevant data, reduce bias, and increase reproducibility.
AI isn't replacing researchers—it’s empowering them with tools that can manage scale, speed, and complexity in ways manual methods cannot match.
Pexels image[/caption]
Medical research is at the heart of clinical advancement. Whether evaluating the safety of new treatments or analyzing trends across patient populations, the integrity and efficiency of research practices have direct implications on healthcare delivery. Among the most labor-intensive tasks in medical research are literature reviews and meta-analyses—two foundational methodologies that aggregate findings from multiple studies to draw broader, evidence-based conclusions.
With the volume of published medical literature increasing exponentially each year, traditional methods of reviewing research have become less sustainable. Today, artificial intelligence (AI) is beginning to play a transformative role in this process, offering ways to streamline literature searches, extract relevant data, reduce bias, and increase reproducibility.
AI isn't replacing researchers—it’s empowering them with tools that can manage scale, speed, and complexity in ways manual methods cannot match.
Ali M. Fazlollahi[/caption]
Ali M. Fazlollahi, MSc, McGill Medicine Class of 2025
Neurosurgical Simulation and Artificial Intelligence Learning Centre
Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital
Faculty of Medicine and Health Sciences
McGill University, Montreal, Canada
MedicalResearch.com: What is the background for this study?
Response: COVID-19 disrupted hands on surgical exposure of medical students and academic centres around the world had to quickly adapt to teaching technical skills remotely. At the same time, advances in artificial intelligence (AI) allowed researchers at the Neurosurgical Simulation and Artificial Intelligence Learning Centre to develop an intelligent tutoring system that evaluates performance and provides high-quality personalized feedback to students. Because this is the first AI system capable of providing surgical instructions in simulation, we sought to evaluate its effectiveness compared with learning from expert human instructors who provided coaching remotely.
Dr. Lee[/caption]
Cecilia S. Lee, MD, MS
Associate Professor,Director, Clinical Research
Department of Ophthalmology
Harborview Medical Center
University of Washington Seattle, WA
MedicalResearch.com: What is the background for this study?
Response: Cataract is a natural aging process of the eye and affects the majority of older adults who are at risk for dementia. Sensory loss, including vision and hearing, is of interest to the research community as a possible risk factor for dementia, and also as a potential point of intervention. Because cataract surgery improves visual function, we hypothesized that older people who undergo cataract surgery may have a decreased risk of developing Alzheimer disease and dementia.
We used the longitudinal data from an ongoing, prospective, community based cohort, Adult Changes in Thought (ACT) study. The ACT study includes over 5000 participants to date who are dementia free at recruitment and followed until they develop Alzheimer disease or dementia. We had access to their extensive medical history including comprehensive ophthalmology visit data. We investigated whether cataract surgery was associated with a decreased risk of developing Alzheimer disease and dementia. 
Dr. Yun Liu[/caption]
Yun Liu, PhD
Google Health
Palo Alto, California
MedicalResearch.com: What is the background for this study? Would you describe the system? Does it use dermatoscopic images?
Response: Dermatologic conditions are extremely common and a leading cause of morbidity worldwide. Due to limited access to dermatologists, patients often first seek help from non-specialists. However, non-specialists have been reported to have lower diagnostic accuracies compared to dermatologists, which may impact the quality of care.
In this study, we built upon prior work published in 
Dr. Fornwalt[/caption]
Brandon K Fornwalt, MD, PhD
Associate Professor, Director Department of Imaging Science and Innovation
Geisinger
MedicalResearch.com: What is the background for this study?
Response: Atrial fibrillation (AF) is an abnormal heart rhythm that is associated with outcomes such as stroke, heart failure and death. If we know a patient has atrial fibrillation, we can treat them to reduce the risk of stroke by nearly two-thirds. Unfortunately, patients often don’t know they have AF. They present initially with a stroke, and we have no chance to treat them before this happens. If we could predict who is at high risk of either currently having AF or developing it in the near future, we could intervene earlier and hopefully reduce bad outcomes like stroke. Artificial intelligence approaches may be able to help with this task.
Dr. Etkin[/caption]
Amit Etkin, MD, PhD
Department of Psychiatry and Behavioral Sciences
Wu Tsai Neurosciences Institute, Stanford Universitu
Stanford, CA
MedicalResearch.com: What is the mission of Cohen Veterans Bioscience - CVB?
Response: Cohen Veterans Bioscience (CVB) is a non-profit 501(c)(3) research biotech dedicated to fast-tracking the development of diagnostic tests and personalized therapeutics for the millions of Veterans and civilians who suffer the devastating effects of trauma-related and other brain disorders.
MedicalResearch.com: How can patients with PTSD or MDD benefit from this information?
Response: With the discovery of this new brain imaging biomarker, patients who suffer from PTSD or MDD may be guided towards the most effective treatment without waiting months and months to find a treatment that may work for them.
MedicalResearch.com: What is the background for this study?
Response: This study, which was supported with a grant from Cohen Veterans Bioscience, grants from the National Institute of Mental Health (NIMH and other supporters, derives from our work over the past few years which has pointed to the critical importance of understanding how patients with a variety of psychiatric disorders differ biologically. The shortcomings of our current diagnostic system have become very clear over the past 1-2 decades, but the availability of tools for transcending these limitations on the back of objective biological tests has not kept pace with the need for those tools.
In prior work, we have used a variety of methods, including different types of brain imaging, to identify brain signals that underpin key biological differences within and across traditional psychiatric diagnoses. We have also developed specialized AI tools for decoding complex patterns of brain activity in order to understand and quantify biological heterogeneity in individual patients. These developments have then, in turn, converged with the completion of a number of large brain imaging-coupled clinical trials, which have provided a scale of these types of data not previously available in the field.
Dr. Gerstung[/caption]
Moritz Gerstung PhD
Group Leader: Computational cancer biology
EMBL-European Bioinformatics Institute
MedicalResearch.com: What is the background for this study?
Response: We have learned a lot in the last ten years about the molecular nature about various cancers thanks to the resources created by TCGA, ICGC and many other initiatives. Similarly, digital pathology has progressed hugely due to new AI algorithms. Yet it hasn’t been explored deeply how a cancer’s genetic makeup and its histopathological appearance are related. Here computers can be very helpful as they can process large amounts of digital microscopy slide images and test whether there are any recurrent histopathological patterns in relation to hundreds or thousands of genetic and other molecular abnormalities. 
