While artificial intelligence (AI) does not replace human clinicians in their work related to cancer prevention, detection and treatment, some of AI’s most powerful applications have already transformed the oncology landscape. From enhancing diagnostic capabilities in pathology and radiology and enabling more personalized, connected and value-driven care by matching care pathways to each patient’s unique needs, AI's impact is already visible across oncology.
This is notable as global cancer rates continue to soar, and clinical resources may be limited. By 2050, the number of new cases is expected to reach 33 million annually. In addition, cancer is still one of the most expensive conditions facing workplaces today, with 88% of employers ranking it as a top condition driving health care costs in 2025, according to Business Group on Health’s 2026 Employer Health Care Strategy Survey.
Employers should take note, because both opportunities and challenges exist in terms of how AI can improve patient outcomes and at times, lower costs. AI-informed applications in oncology have the potential to lead to longer survival rates, better patient quality of life and increased employee productivity. Yet, the use of AI to support patient care requires robust safeguards and can also, at least in the initial stages, come with higher price tags and new complexities. In addition, not all AI applications are designed to improve patient care, the technology is also used to support provider practices in improving billing accuracy, maximize the cost and number of procedures billed and, by improving provider productivity, can also lead to higher number of more complex services and procedures performed, contributing further to the increase in cost of cancer care and health care in general.
Earlier and more precise cancer detection
Artificial intelligence already boosts how and when cancer is detected, specifically through advanced screening tools and predictive modeling. AI can analyze medical images and data faster than humans and detect what eludes the human eye. A quicker and more accurate diagnosis means better treatment options and results.
More specifically, AI has a higher capacity for discovering abnormalities in biopsy samples or radiological images, such as those used in mammography. In digital pathology, AI tools aid in risk prediction and facilitate the examination of extensive tissue sample sets for patterns and markers, simplifying the pathologists’ tasks.
Considerations for Employers
Employers should hold their health plan, solution providers and consulting partners accountable for understanding how AI-powered screening programs could help increase early detection rates as this space develops.
More personalized treatments
Clinicians are using artificial intelligence to analyze genetic and clinical data – sometimes massive biological datasets – to deliver real-time insights, inform decision-making and create more personalized treatments.
AI can help clinicians match patients with the most effective therapies, recommend targeted drugs based on genetics, optimize radiation therapy plans, predict which patients will respond to immunotherapy and suggest clinical trials based on patient profile. As a result, patients receive tailored treatments that have a greater likelihood of success.
It also can help to ensure effective ongoing care, by tracking patient data and monitoring early signs of recurrence, among other tools.
Considerations for Employers
More personalized treatment has direct implications on cost and care outcomes. Employers should confirm that oncology partners are using validated, evidence-based AI tools and are actively addressing their known risks (e.g., hallucinations, lack of peer review). Employers should also ensure safeguards are implemented to prevent AI from creating access barriers to advanced diagnostics and therapies, and clarify that health plans are using AI capabilities in utilization management in ways that protect the patient experience, privacy and related legal considerations.
Accelerated drug discovery
Bringing a new drug from concept to clinic is a long and costly endeavor, often stretching 10 to 17 years and nearing $2.8 billion. Even after such investments, the odds are daunting: Only about one in 10 compounds tested in clinical trials ultimately reaches the market.
AI has started to streamline drug discovery, one of medicine’s most complex pipelines, by identifying promising lead compounds, predicting how drugs will behave in the body and uncovering new uses for existing therapies. Over the past decade, several AI-developed drugs have advanced into clinical trials, an early but meaningful signal of change.
Considerations for Employers
While AI may help to create a faster, more efficient path to discovering new treatments for cancer and other diseases, often these treatments can come with high costs. Employers should work with their health plan, vendors and PBMs to understand the downstream impact of new cancer treatments and ensure that the new, more expensive treatments are used only when more established lower cost therapies are not adequate.
AI’s Next Chapter in Oncology Care
As artificial intelligence augments clinical judgment, it has the potential to reshape not only how cancer is detected and treated, but also how employers design benefits and workforce support. Because cancer remains one of the most significant global health challenges, AI is already influencing the cancer journey for employees, survivors and caregivers.
Employers and their partners that pair responsible AI-enabled cancer care with thoughtful, ongoing support for those living with or beyond cancer, as well as the people who care for them, will be better positioned to improve access to quality care, outcomes, sustain productivity and build a more resilient workforce.
