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By leveraging modern health care technologies and data-driven insights, AI and machine learning have the potential to enhance screening, diagnosis, and personalized treatment strategies across multiple cancer types.
The integration of artificial intelligence/machine learning (AI/ML) into oncology is revolutionizing the delivery of cancer care. The development of scalable algorithms is slowly extending into real-world clinical applications. By leveraging modern health care technologies and data-driven insights, AI/ML has the potential to enhance screening, diagnosis, and personalized treatment strategies across multiple cancer types. In March 2024, the National Cancer Institute published a webinar as part of the Events: AI in Cancer Researchseries.During the session, William Lotter, PhD, assistant professor at Dana-Farber Cancer Institute, described the impact of AI/ML tools on daily clinical workflow and the delivery of care to patients with cancer. Below, I explore how AI/ML is reshaping oncology, with a focus on pivotal shifts and clinical applications.
The expanding role of AI/ML in oncology is driven by 2 key shifts: technological advancement and the digitization of health care data. The first involves the advent of sophisticated computational tools, especially deep learning. Over the past decade, deep learning has leveraged algorithmic models to identify intricate patterns in real-world data, enabling breakthrough applications in medical imaging, genomics, and molecular oncology. Coupled with advancements in graphical processing units and cloud computing, AI systems are becoming faster, more accurate, and scalable.
The second shift involves the digital transformation of oncology. Patient data is now stored in electronic health record (EHR) systems, radiology and pathology images have been digitized, and genomic profiling is increasingly being standardized with evidence-based guidelines to streamline clinical practice. This abundance of high-quality, longitudinal data is critical for building AI/ML models tailored to real-life patient care. Eventually, real-time access to such data will enable personalized AI predictions, further advancing the goals of precision oncology.
As of July 2023, 692 AI algorithms have been FDA-cleared for medical use, and over 500 of these are for radiology applications. Several categories of AI/ML algorithmic tools are particularly relevant to oncology:
The integration of AI/ML technologies into daily clinical workflow is transforming the landscape of cancer care. As society continues to witness advancements in computational power, data accessibility, and algorithmic development, the role of AI/ML in oncology will only expand. However, realizing the full potential of these powerful tools requires collaboration among clinicians, researchers, administrators, computer scientists, and policymakers to address challenges such as data standardization, ethical concerns, and regulatory approvals. Ultimately, cancer care delivery will continue to be reshaped by AI/ML, ensuring that patients benefit from earlier detection, more accurate diagnoses, and better-informed treatment strategies. By embracing this digital revolution, the oncology community is taking a meaningful step toward the vision of precision medicine and improved outcomes for patients worldwide.
Nicole A Colwell, MD, is a senior editor/medical writer for the Association of Cancer Centers.
Understanding both the benefits to be had from offering BsAb therapy to its community and the scope of this undertaking, Vanderbilt-Ingram Cancer Center took a novel approach with its APP-led BsAb step-up dosing clinic, which won the program a 2026 ACCC Innovator Award.
