How AI is Revolutionizing Community Oncology Care

Artificial intelligence (AI) based tools are poised to become indispensable assets in community oncology, offering practical solutions for synthesizing research, organizing patient data, and monitoring treatment responses. According to Dr. Jim Chen, a medical oncologist at Genesis Cancer and Blood Institute, these technologies can help alleviate the significant time constraints faced by community providers, enabling them to deliver better, more tailored care to their patients.

The Challenge for Today's Community Oncologist

Speaking at the inaugural MiBA Community Summit, Dr. Chen highlighted the information overload that is common in the field. "We face a lot of challenges, including thousands of papers per year and having to compile hundreds of notes," he said. "There’s a lot of information we have access to, but thinking about when and how to access it in a timely manner in a clinical setting is challenging."

This challenge is compounded by the need to ensure any selected treatment plan complies with institutional guidelines and drug availability, adding layers of complexity to every patient decision.

How AI Steps in to Support Cancer Care

Dr. Chen explained that modern AI, built on large language models, has evolved far beyond the early "expert systems" of the 1950s. Today's AI can process and respond to human language, making it a powerful tool for community oncologists.

Key applications and takeaways from the discussion include:

• Comprehensive Data Management: AI systems excel at gathering patient-specific data, reviewing existing literature, taking notes, and tracking patient updates.
• Clinical Decision Support: AI-powered chatbots can serve as valuable support tools by providing additional clinical context using notes input by the oncologist.
• Enhanced Professional Development: These tools will not replace oncologists. Instead, they will reduce time spent on mundane tasks, allowing professionals to focus on complex care and development.

The Oncologist's Role in the Age of AI

While demonstrating how an AI-enabled chatbot can be used, Dr. Chen stressed that human oversight is critical. It is the responsibility of the clinician to check for "hallucinations"—responses generated by the AI based on word probabilities rather than factual information. This remains a significant drawback of current AI systems.

Despite this limitation, AI tools are already proficient at managing information within electronic medical records (EMRs). They can efficiently handle data entry and extract key information, making it readily available for oncologists. As Chen noted, oncologists want AI to provide "real-time treatment guideline prompts, automating toxicity monitoring, predictive analytics for preventing disease progression, and administrative and documentation support."

The Future of AI in Oncology Practice

The application of AI in oncology is expanding rapidly. A recent literature review published in The Oncologist showed that the number of studies on large language models in cancer care increased significantly between 2022 and 2024. These tools are being evaluated for health information access, clinical diagnosis, and disease management, with the potential to improve accuracy and reduce provider workload.

However, Dr. Chen concluded by emphasizing that AI will never fully replace the role of a trained oncologist. "AI is definitely a tool; it’s not going to take our jobs," he explained. "It’s supposed to provide support in the things that you find mundane or that split your attention. AI can help you in your development." By automating routine tasks, AI frees up clinicians to focus on what matters most: delivering personalized, expert care to their patients.

References

1. Chen J, Hilton B. The role of AI in community practice oncology. Presented at: MiBA Community Summit; September 27-28, 2025; Scottsdale, Arizona.
2. Chen D, Avison K, Alnassar S, Huang RS, Raman S. Medical accuracy of artificial intelligence chatbots in oncology: a scoping review. Oncologist. 2025;30(4):oyaf038. doi:10.1093/oncolo/oyaf038