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3 things to know about AI and cancer care
Artificial intelligence (AI) has the potential to shape nearly every aspect of our lives – including cancer care. At MD Anderson, we see the opportunities and risks associated with the emergence of AI and an ever-increasing amount of data being generated by our cancer research, patient care and clinical operations.
“AI has promise to improve outcomes and experiences for our patients,” says Caroline Chung, M.D., vice president of Data Impact and Governance and chief data officer. “However, simply enabling AI capabilities within a technology or workflow because they are available will not necessarily drive to impact and is not our approach. We are focused on utilizing the right data-driven decision-making across all our mission areas.”
As a clinician-scientist and director of data science development and implementation for MD Anderson’s Institute for Data Science in Oncology, Chung is an active voice within AI communities and consortia that span federal institutions, academic organizations and industry partners. She’s active in National Institutes of Health (NIH) AI workshops and leads the AI Community of Practice for ASCO, among others.
Here, Chung shares how our expert clinicians, scientists and technology teams are guiding MD Anderson’s approach to AI and what’s on the horizon for our patients.
Data quality determines impact
“If you drive a luxury sports car with a powerful engine and you fill up the tank with regular gasoline instead of premium, your car’s performance will be subpar and over time you’ll damage the engine,” says Chung. “The same goes for the data that is used to fuel AI systems and models.”
AI technology solutions learn patterns and make predictions based on processing and analyzing large amounts of data. To ensure that the data MD Anderson uses will yield good results when coupled with AI, Chung has steered the organization to focus on laying foundational elements and governance approaches.
“What we can accomplish with AI depends upon data quality – ensuring that when data is captured it has the appropriate context and metadata, can be found easily and understood so that it is appropriately used,” says Chung. “We also acknowledge that not all data is useful for all purposes. To get answers for important questions, we must consider the context and focus on the most opportune and useful data for development and implementation of solutions.”
Chung ensures teams across MD Anderson contribute to a data ecosystem that encompasses not only data but also technologies, processes, policies, culture and people. This holistic approach leads to collaboration and connectivity — from data generation through to clinical impact — avoiding siloed clusters of data and ensuring we can unlock the power of complex information to advance our mission to end cancer.
We’re guided by impactful questions to yield meaningful changes
As an associate professor in Radiation Oncology and Diagnostic Imaging with a clinical practice focused on central nervous system malignancies and a computational imaging lab focused on quantitative imaging and modeling to detect and characterize tumors and toxicities of treatment, Chung regularly leverages AI-based technology to enable personalized cancer treatment.
“Across MD Anderson we are evaluating uses for AI with the aim of driving greater efficiencies, novel insights and where we can make the most impact,” says Chung. “Rather than asking what can we do with AI, we are critically asking if AI is an effective solution to the particular prioritized goal – considering the teams involved, processes, data and technology.”
AI is a broad umbrella term that covers different approaches of using computer science and data to enable problem solving in machines, such as:
- generative AI
- deep learning
- natural language processing
- predictive analytics
- data algorithms
At MD Anderson, our interdisciplinary teams across Technology, Data and Innovation, clinical operations, research, and patient care are using all of these to potentially develop at-scale solutions for patients and clinical providers. Questions we’re trying to answer using AI include:
How can we lessen physicians’ administrative burden so they can spend more 1-on-1 time with patients and less time in front of a computer screen?
An ambient listening pilot aims to relieve administrative burden by using generative AI to capture discussions between providers and patients to chart and document outpatient visits with notes in patients’ electronic health records.
How can we reduce patient falls, the most commonly reported patient safety incident in acute hospital settings, according to the NIH?
A predictive analytics tool is in development to identify our most at-risk patients based on a host of health factors to support operational and clinical decision making and ultimately lessen the potential for patient falls to occur.
How can we share our clinical expertise in radiation therapy – a core treatment option for many cancer patients – with underserved countries?
Our teams have developed novel deep learning technologies to bring MD Anderson’s expertise to communities who could benefit from our standards of care but are unable to access them due to distance and a lack of resources.
As one of the largest providers of oncology clinical trials, how can we connect patients more efficiently with options tailored to their unique diagnoses?
By using natural language processing, automatic review of specific provider notes and other data in our electronic health record system, we seek to expedite patient matches and enrollment in relevant clinical trials.
With more than 15,000 surgical cases manually scheduled each year, how can we address inefficiencies that can lead to delays in care?
Novel algorithms are in development to accurately predict surgery length and time spent in outpatient care. The goal is to yield more efficient surgical workflows and reduce patient wait times.
Ensuring safe and responsible use is imperative
“With the promise of AI solutions to impact clinical decisions and the use of patient data, coupled with an evolving regulatory landscape, we are choosing to establish a strong foundational approach to AI that assures it will positively impact our patients, our team members and our mission,” says Chung. “There is no quick route or shortcut.”
Make no mistake: there is a sense of urgency to leverage AI. At MD Anderson, we’re balancing this excitement for the future with a safe, sustainable, ethical, value-based and risk-managed approach to protect patient data and avoid bias. And with this emergence, the role of human decision-making and how humans interact with this technology to understand, validate and verify results is a critical aspect.
“As a leader in cancer care and research, we have the responsibility to harness AI’s potential to make an impact on quality of care, patient safety, research and streamlined operational processes,” says Chung. “This is an exciting time and it’s only the beginning.”
Learn about MD Anderson’s Institute for Data Science in Oncology.
Advancing cancer surgery through data science
At MD Anderson, operating room lamps cast a brilliant glow on surgeons, nurses, anesthesiologists and other clinical team members as they work together to treat thousands of patients each year. Now that glow has been cast wider to include data scientists and engineers. Led by Jeff Siewerdsen, Ph.D., these quantitative scientists are regularly suiting up in scrubs to experience first-hand the operating room workflows they’re trying to improve.
Desire to make a positive impact leads to role at MD Anderson
MD Anderson’s Surgical Data Science Program was born from Siewerdsen’s observations over his 25 years as an academic researcher. In that time, he focused on developing new imaging technologies for diagnostic and interventional procedures. While his work produced numerous technologies and algorithms now used in operating rooms, he has strived more recently to conduct his research more closely with clinical teams impacted by the problems he has sought to address.
“Rather than continuing to add new technologies to address unmet clinical needs, I wanted to simplify, integrate and critically evaluate the value of new technologies using data science and systems engineering,” says Siewerdsen.
That opportunity arrived last year when MD Anderson recruited him.
“I was drawn to MD Anderson’s vision, strategic resources, expertise and capacity to bring major positive impact for patients and clinical teams,” recalls Siewerdsen, who was recently named to the National Academy of Inventors 2023 Class of Fellows. “To bring data science and systems engineering approaches to surgery – at MD Anderson’s scale – is a tremendous opportunity to show how these disciplines can make a tangible impact for patients and their clinical teams.”
Enabling surgery advances that benefit patients and clinicians
Drawing inspiration from his research as well as the “Surgineering” education program that he created at his previous institution, Siewerdsen established and leads a focus area within the newly launched Institute for Data Science in Oncology (IDSO). The IDSO Safety, Quality and Access focus area fosters collaboration with surgeons and clinical departments to integrate new technology and drive data science solutions to clinical practice.
One example is the creation of computational tools for improved operating room scheduling to enhance the efficiency of operating room use, leading to increased patient access and improved clinician wellness by streamlining clinical workflows. Another example is to use machine learning for real-time analysis and prediction to avoid surgical adverse events. A third involves surgical process modeling to refine workflows and quantitatively evaluate the benefit of emerging technologies before introducing them to the operating room.
“In the years ahead, my goal is not only to help move the needle on safety and quality but also to prove the hypothesis that quantitative scientists integrated with clinical operations are key to realizing major advances in surgery,” says Siewerdsen. “For MD Anderson’s patients, this means that surgery will be more accessible, safer and will use the most cutting-edge technologies to their fullest benefit.”
What does a data scientist do?
Theoretically, anyone who analyzes data to do science could call themselves a data scientist. But to me, that term also implies the use of computers. So, it’s data plus computers that makes someone a data scientist in my mind.
I also tend to apply a slightly narrower definition: I think of a data scientist as someone who’s concerned with deriving hidden knowledge from data trends and then making predictions based upon them.
To accomplish those goals, you need two things. The first is the ability to handle, organize, standardize, label, test, move and make data amenable to analysis. The second is the ability to make predictions based on that data, and to develop the artificial intelligence (AI) tools needed to analyze it, learn from it and evolve as an organization.
What makes a good data scientist?
You don’t have to be an oncologist to be a good data scientist. In fact, very few data scientists at MD Anderson come from an oncology background. My team consists of everything from astrophysicists to shopping website analysts. But many of their skillsets are completely transferrable, so I am thrilled to have their talents here.
I didn’t even study oncology myself — just pure computer science and molecular biology. I applied data science to drug discovery when I started out in the biotech industry. It wasn’t until I became junior faculty in academia that I started developing the oncology knowledge I use today.
It’s easy to fall into the trap of thinking you’re poised for success in the field of data science, just because you’ve gotten some training on the most recent technologies. But the tools being used today are very different from the tools that will be used two years from now. So, if all you know how to do is push buttons on the latest fad, you’re going to get lost straightaway.
To be a good data scientist, you’ve got to have a good grasp of the fundamentals of math and computer science — and a really solid understanding of the underlying methodologies to identify trends and make predictions. You’ve also got to understand the limitations of any tools you’re using and how to design questions to make sure your experiment is both unbiased and testing the actual hypothesis.
“Bilingual” people who can “speak” both oncology and data science — and take complex biological problems and translate them into computational questions — are what I call “translational” data scientists. That’s what I consider myself. And that’s what I try to help each of my new team members to become, if they’re not one already.
How MD Anderson is harnessing the power of data
As a drug discovery scientist trained in molecular biology, I’ve always been fascinated by the idea of doing things at scale. Bringing all the data together and identifying hidden patterns in it that no one else can see — then using those insights to inform drug discovery efforts — is much more satisfying to me than trying to find the answers to very specific questions. But we need both types of science to advance medicine, of course.
I just love the process of drug discovery. It brings together experts from so many different disciplines, including genomics, physics and chemistry, to name a few. It’s a really complex field.
It’s also exciting to be exploring drug discovery here at MD Anderson, where I get to work with people like Andy Futreal, Ph.D., who is leading initiatives to collect data and profile patients in really meaningful ways; and with Tim Heffernan, Ph.D. ,who is leading our Therapeutics Discovery teams to explore new ideas through experiments that lead to the development of new drugs.
MD Anderson already has so many phenomenal initiatives— like the Patient Mosaic™ — that don’t exist anywhere else. All that was lacking was a cohesive way to harness its collective data to effectively drive our decision-making. That’s why I was recruited: to develop a kind of “information superhighway” that sits right in the middle, allowing a continuous feedback loop to keep us all on track.
Although it’s still early, we have already been able to harness knowledge from our rare tumor patient samples and identify potential “Achilles heels” genes using our cutting-edge AI methods. We then demonstrated these genes’ importance using our Therapeutics Discovery's translational biology expertise, and we are moving them to the drug discovery stage. This shows how MD Anderson’s unique capabilities enable us to quickly change the way we make advances that benefit patients.
As a co-lead for Computational Modeling for Precision Medicine in our Institute for Data Science in Oncology (IDSO), I’m spearheading that initiative with the Adaptive AI-Augmented Drug Discovery and Development program, “A3D3a.” I contrived the name deliberately so that we could call it “Ada.” It’s a tribute to one of my heroes, Ada Lovelace, the world’s first computer programmer.
The daughter of British aristocrat and Romantic poet Lord Byron, Lovelace worked with inventor Charles Babbage on machines that could make calculations at great scale. Then one day, she said to him, “Why don’t we make a machine that can be programmed to do whatever calculation we want it to?” She wrote the first computer program and with that, the era of computer programming was born.
Why I joined MD Anderson
The collective brain power at MD Anderson is truly unequaled. It simply doesn’t exist anywhere else. Neither does the ability to make advances benefit patients more quickly. That’s why I believe MD Anderson is the only place on the planet where we can do this. But to be successful, our work needs to start and end with the patient. That means:
- coming up with each hypothesis based on the existing patient data
- validating it in an experimental setting relevant to patients
- taking it to the preclinical and clinical trial stages in our own hospital
- bringing it to our patients at the bedside and in the clinic, and
- using the feedback generated by that process to refine any new drug therapy or patient care practices.
For me, personally, that also means developing algorithms to help us learn more about cancer, and uncovering new information that can further refine our decision-making processes. Using AI to inform each and every one of the thousands of decisions our faculty and staff make each day is what I’ve dedicated my career to — and precisely why I joined MD Anderson.
A lot of drugs that get approved to treat cancer today are considered “me, too” drugs. This means they ride the coattails of the ones that came before them. But when you see a brand-new drug that you helped develop enter a Phase I clinical trial to be tested for the first time — and you know that it will soon start directly benefiting patients — it really is the most exciting thing in the world. That’s where I get my buzz every morning, and it’s my favorite part of the job.
It’s too soon for anything I’ve been working on here to be entering Phase I clinical trials yet. But by applying data science, we’ve already identified several targets for potential therapy development in very rare cancers, such as metastatic uveal melanoma, which is really difficult. And that’s exactly the kind of thing we can only do at MD Anderson, because you need all the unique components of the entire pathway in one spot.
Now, with the help of AI knowledge bases like the CanSAR platform I created, we’ll soon be able to make drug discoveries that even places like MD Anderson couldn’t possibly have made on their own.
Related Articles and News
MD Anderson News Releases
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MD Anderson’s Institute for Data Science in Oncology establishes internal advisory council to maximize impact
June 2024
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MD Anderson’s Institute for Data Science in Oncology announces appointment of inaugural IDSO Affiliates
March 2024
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MD Anderson, TACC and the Oden Institute announce funding for the next round of collaborative cancer research projects
December 2023
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Collaboration on Data and Computational Sciences Announces Next Round of Projects to Advance Cancer Breakthroughs
November 2021
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MD Anderson advances data collaboration through technology agreement with Syntropy
April 2021
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MD Anderson and UT Austin collaboration to end cancer welcomed enthusiastically by state and federal stakeholders
November 2020
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"Cancer Needs a robust 'metadata supply chain' to realize the promise of artificial intelligence"
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"Realizing the power of data science to advance cancer research and cancer care"
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