AI and health equity in cancer: 3 areas of progress
December 13, 2024
By Alison Bert, DMA
Dr Judy Gichoya (left) supports the interventional radiology medical team in Rwanda, led by Dr Ivan Rukundo. She shared this image with permission in her presentation as one of three panelists on the Lancet Webinar AI and Health Equity in Cancer.
On a recent Lancet Webinar, three experts reveal how they’re using AI to advance cancer diagnosis and treatment — and the challenges of making these advances accessible to all
Prof Jakob Kather’s research team at Else Kröner Fresenius Center for Digital Health at TUD Dresden University of Technology in Germany is using AI to revolutionize oncology treatment through biomarker extraction.
Dr Karin Dembrower, Senior Breast Radiologist at St Göran’s Hospital in Sweden, is overseeing a program using AI technology for breast cancer screening with promising results.
Dr Judy Gichoya, an Associate Professor at the Emory University School of Medicine and a leading figure in healthcare AI and translational informatics, is exploring how AI can be used to address global health challenges and optimize resource allocation for maximum impact.
The three experts at the intersection of medicine and computer science were featured on a recent webinar presented by The Lancet Group: AI and Health Equity in Cancer opens in new tab/window. Moderated by Dr Ben Abbott opens in new tab/window, Executive Editor of The Lancet opens in new tab/window, and Dr Rupa Sarkar opens in new tab/window, Editor-in-Chief of The Lancet Digital Health opens in new tab/window, the forum explored the opportunities and challenges of using AI in cancer care and oncology, with an emphasis on health equity. Here are some highlights.
AI in precision oncology
Prof Jakob Kather stressed the importance of interdisciplinary collaboration and varied perspectives in driving innovation, highlighting the diverse backgrounds within his own research team. His Clinical Artificial Intelligence research group opens in new tab/window, located at EKFZ for Digital Health, is part of the Faculty of Medicine and Faculty of Computer Science at TUD Dresden University of Technology and is also affiliated with the Department of Medical Oncology at the National Center for Tumor Diseases Heidelberg.
Dr Jakob Kather opens in new tab/window is Professor of Clinical Artificial Intelligence at TU Dresden and an expert at the forefront of AI and precision oncology. In his presentation, he shed light on the transformative potential of AI in oncology treatment through biomarker extraction, methodological advancements, and the application of generalist models, all while emphasizing the significance of interdisciplinary research in propelling innovation.
To illustrate an important application of AI, Prof Kather spoke of the need to choose from a mounting number of treatment options. “One of the problems is that oncology is getting more and more complex,” he said, citing the proliferation of guidelines for treating lung cancer.
Prof Jakob Kather, PhD
“In 2010 … we had very limited options; there were only a certain number of chemotherapy drugs available,” he said. “But in 2023, these guidelines were much, much more extensive. If you now look at the guidelines for how we treat a patient, for example, with lung cancer or any other cancer, then you can see that we find these huge decision trees.”
“So we have to make many, many decisions to choose between many different types of treatments. The question is: How do we make this decision? One thing that can help us with this is biomarkers. A biomarker is something that you measure in cancer tissue or in cancer patients that helps you make the right treatment decisions and prescribe the right tree.”
Delving into the practical application of generalist models like ChatGPT in oncology, Prof Kather emphasized the need for additional context to enhance responses: “We need to use it in the right way in order to explore what this technology can do for us.”
AI for breast cancer screening
In her presentation on AI integration in breast cancer screening, Dr Karin Dembrower opens in new tab/window, Senior Breast Radiologist and Head Physician at St Göran’s Hospital and a researcher at the Karolinska Institute, shared insights into the implementation of AI technology in clinical workflows for breast cancer screening, while emphasizing the critical need for thoughtful consideration of equity, validation and bias mitigation in AI implementation.
She began by providing an overview of the Swedish breast cancer program opens in new tab/window, which invites women ages 40 to 74 for free breast screenings every two years. Detailing the transition to AI-integrated screening, Dr Dembrower explained the workflow changes, highlighting the replacement of one human reader with a commercial AI algorithm at her hospital. She noted the promising results of a prospective clinical study involving over 55,000 women, which resulted in increased cancer detection rates and improved positive predictive values (PPV) with AI integration.
Karin Dembrower, PhD
Reflecting on the equity considerations in AI implementation, Dr Dembrower stressed the importance of diverse training datasets and robust validation processes to address disparities. She raised concerns about access to AI-integrated screening, noting disparities in awareness and attendance based on socioeconomic status and cultural factors.
“We want to have an AI algorithm that is trained on a diverse population where minorities are represented,” she said. “And it’s not always easy to get access to diverse datasets. And that is something we have to be aware (of) and take into account the validation process …”
Dr Dembrower also addressed the challenges around access. For example, in Sweden there are only a few hospitals working with AI-integrated screening. “Although women can decide where they want to be screened,” she said, “our experience is that women with more resources (are) more likely to find out where AI is integrated … compared to women with lower social economic status. And we have also noticed that there is a lower attendance in the screening program of women with lower socioeconomic status because of language, culture and an inability to access the screening facility.”
Where does AI bring the most ROI?
“I challenge us to also think, ‘Where does AI bring the most return on investment, and what’s the opportunity cost?’”
JG
Judy Gichoya, MD, MS
Associate Professor Department of Radiology and Imaging Sciences at Emory University
Prof Judy Gichoya opens in new tab/window, who co-leads the Healthcare AI Innovation and Translational Informatics Lab opens in new tab/window at Emory University, explored the intersection of AI and health equity on a global scale.
Reflecting on the disparities in cancer mortality rates across different regions, Dr Gichoya posed a challenging question:
I challenge us to also think, ‘Where does AI bring the most return on investment, and what’s the opportunity cost?’
Prof Judy Gichoya, PhD
Dr Gichoya underscored the potential of AI in enhancing patient engagement and improving access to healthcare information: “Today, you don’t need to rely on English as the only language where you can get information. You can ask questions: Where can I get screening? How do I plan for my screening?
“But I want to stop and pause here because when you think about these large language models — this is one form of AI that can be used especially for patient engagement — we see that the answers that are represented represent the majority opinion.”
Because guidelines differ around the world, for example, “it’s difficult to say what guidelines should you use.”
She pointed out that because LLMs learn from preexisting patterns, they’re not always applicable universally. She mentioned “historical biases that have pervaded in our medical literature” and challenges involving health equity.
“It’s very difficult to translate AI across different settings,” she said. For example, “we still find, disparities where we see Black women tend to have still worse outcomes. And it is a cascade effect from who gets screening and to who gets treatment. And so just fixing one problem is not going to end up saving our lives.”
Dr Gichoya raised thought-provoking questions about the practical implications of AI implementation in diverse healthcare settings and the need for tailored solutions that consider local contexts and guidelines.
She highlighted the complexities of translating AI advancements across different healthcare settings and populations, emphasizing the importance of considering the broader implications of AI implementation beyond diagnosis. She challenged the audience to critically evaluate the impact and resource allocation associated with AI development and implementation, urging a thoughtful approach to maximize the benefits of AI in healthcare.
“There’s always an opportunity cost,” she said. “So what after you get an AI diagnosis? Can we treat? Do we have the necessary resources? And where are we diverting resources when we spend all this amount of money on AI?”
She urged a nuanced approach to AI development and regulation to ensure equitable and effective use of technology in improving global health outcomes.
Contributor
ABD