Artificial intelligence (AI) is being used more and more in cancer care. It can handle large amounts of medical data and provide useful information. At the 2025 Gordon Ginder Innovations in Cancer Symposium, experts showed how AI and data science help research and patient care. They said AI is powerful but should be used carefully with good science and ethics in mind.
Dr. Robert A. Winn, Director of the Massey Cancer Center, said AI is a big new tool but humans still need to guide it with their knowledge. Hoifung Poon from Microsoft Health Futures pointed out that AI can read complex medical data and do tasks that were not possible before. AI can examine multiomics data, like genes and proteins, opening new ways to tailor cancer care to each patient.
Caris Life Sciences uses advanced AI to help with precision medicine by studying molecular details. Their system links over 580,000 patient records with molecular markers to find cancer types and predict how each patient will react to treatments. This helps doctors make better treatment plans aimed at each patient’s unique tumor. Diane Davis, an ovarian cancer survivor, says Caris’ AI helped her doctor choose the right treatment for her.
AI has improved how doctors predict and treat cancer. A review of 74 studies found that AI helps with early diagnosis, predicting the future course of the disease, assessing the risk of getting cancer, predicting how well treatment will work, keeping track of disease progress, checking chances of hospital readmission, spotting risks of complications, and estimating chances of death.
Oncology and radiology benefit most from AI tools because they deal with complex information. AI algorithms find early signs of cancer on images or lab results. These early alerts help doctors start treatment sooner, which can save lives.
AI also looks at data like genetics and tumor details to guess how a patient will respond to treatment. Dr. Ted A. James says AI models that mix tumor and genetic information work better than older methods. This helps doctors design treatments that work best with fewer side effects.
Diagnostic imaging has improved with AI. A review of 30 studies since 2019 shows that AI helps analyze images better, speeds up work, supports prediction, and assists doctors in radiology. AI software can spot small problems on X-rays, MRIs, and CT scans that people might miss. This lowers errors caused by tiredness or oversight.
AI speeds up how images are read, so treatment can start faster. Hospitals and cancer centers can also handle more scans at a lower cost. This is helpful for places with limited resources facing many patients in the United States.
AI combines image results with electronic health records (EHR) to give a full view of a patient’s health. This helps predict possible disease changes or problems early.
Even though AI is helpful, there are challenges with ethics and keeping patient data safe. Dr. Maia Hightower stresses that AI use must be responsible. Models should be clear, explainable, and fair to keep trust from doctors and patients.
AI needs good data to work well. Bad or missing data can cause mistakes or make health gaps worse, especially in places with fewer resources. Experts say it’s important for doctors, data scientists, ethicists, and IT workers to work together to build good AI tools.
Security is a big concern. Cyberattacks could put patient data at risk, so strong protection is needed. Also, AI can make wrong guesses or “hallucinations.” This means AI results must be checked carefully and watched all the time.
AI is also changing how cancer clinics run by automating front-office and admin work. Companies like Simbo AI provide AI phone systems to help communication between healthcare staff and patients.
In US cancer clinics, managing appointments and calls takes a lot of time. AI phone systems answer calls, schedule appointments, remind patients about visits, and fill open slots by reaching out to patients unlikely to come. This cuts down no-shows and makes better use of resources, which is important because timely care matters a lot in cancer treatment.
AI also helps with insurance claims and data entry, reducing work for office staff. This lets doctors and nurses concentrate more on patients rather than paperwork, improving overall efficiency.
Other AI tools handle tasks like patient triage, gathering data, and checking if patients follow their treatment plans. Virtual assistants using natural language processing (NLP) give patients 24/7 help by answering questions, reminding them about medicine or visits, and gathering symptom info for doctors.
By automating routine tasks, AI helps clinics run smoother, reduces delays, and keeps patients more engaged. This leads to better patient experience and results.
AI use is growing fast in US cancer care. The AI healthcare market is expected to rise from $11 billion in 2021 to over $187 billion by 2030. Many clinics are adopting AI tools for diagnosis, treatment, and administration.
AI helps cancer centers deal with fewer staff, more patients, and more complex cases. With new AI tools for molecular profiling, imaging, and prediction, US healthcare can offer more personal and effective cancer care.
Big cancer centers and AI companies like Caris Life Sciences work together with more than 45 NCI-designated cancer centers. These partnerships collect large amounts of data that power machine learning models. This improves how cancer is identified and treatment recommendations are made.
AI also helps clinics improve their workflows and admin tasks while keeping patient care in focus. Along with careful use of AI and educating doctors, this can support both the science and the human side of cancer care.
AI use and integration in US cancer care are growing quickly. This is due to better technology, more data, and a strong need to improve how cancer care is delivered and how well patients do. Clinic leaders and IT staff should stay updated and ready to use AI tools that help both care and operations. When used carefully, AI has strong potential to change cancer care, helping patients get timely, personalized, and effective treatment while improving healthcare systems.
The symposium focused on artificial intelligence (AI) and data science, exploring how these advancements can benefit oncology care and improve cancer research and patient outcomes.
Dr. Robert A. Winn is the director and Lipman Chair in Oncology at VCU Massey Comprehensive Cancer Center, who highlighted that scientific wisdom will continue to drive AI advancements.
Hoifung Poon emphasized the opportunity to harness complex models with AI to make sense of medical data and turn impossible concepts into plausible realities for health care.
Maia Hightower discussed the need for responsible AI integration, stressing that ethical challenges must be thoughtfully addressed to ensure effective healthcare governance.
Kevin Byrd presented on using AI to create disease-centric, spatially resolved references for diagnostics and therapeutics, moving towards a virtual atlas of medicine.
Wei Zhou is working on hybrid nano-bio systems, aiming to create real-time bioinformation monitoring technologies, such as wearable or implantable devices for early cancer detection.
Maryellen Giger explained that AI has significantly improved medical imaging techniques over the last 40 years, enhancing the diagnosis of diseases like various cancers.
Lawrence Shulman noted that challenges in healthcare are global, requiring cooperation between clinicians and the AI community to deliver effective care in sub-optimal conditions.
The panel conversation moderated by Guleer Shahab focused on future directions for effectively utilizing AI and data science to enhance cancer science and patient care.
The symposium is named after Gordon D. Ginder, whose legacy includes impactful advancements in cancer treatment, research, and education during his long tenure at Massey.
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