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.