Artificial intelligence (AI)–based tools can be used for various practical applications in oncology, including synthesizing research, organizing patient-related data, and monitoring responses and symptoms, according to Jim Chen, MD. These tools can alleviate the time constraints often faced by community providers, allowing them to offer patients better, more tailored care.
“We face a lot of challenges [in our role, including writing] thousands of papers per year [and having to compile] hundreds of notes,” Chen, a medical oncologist at Genesis Cancer and Blood Institute in Little Rock, Arkansas, as well as a developer at MiBA, said during the inaugural MiBA Community Summit.1 “There’s a lot of information we have access to, [bot] thinking about when and how to access it in a timely manner in a clinical setting [is challenging]. Once we have selected a medication or a trial, making sure it’s in compliance with your institution and which drugs are [actually] available to you [are] all things you have to consider when you make a treatment decision for a patient.”
The Role of AI in Community Oncology Practice: Key Takeaways
- AI-based systems have several clinical applications in community practice, including gathering patient-specific data, reviewing existing literature, taking notes, tracking patient updates, and monitoring treatment-related adverse effects and treatment responses.
- AI chatbots can be used for clinical decision support; these systems can provide additional clinical context using notes input by the oncologist.
- AI tools will not be able to replace the role of the oncologist. Rather, they will aid professional development by reducing the time oncologists spend on mundane or superfluous tasks.
Why Are Oncologists Talking About AI?
Chen began his presentation by explaining noting that the earliest AI systems, which were developed in the 1950s, were comprised of large arrays of predetermined decisions based on prior experience. These “expert systems” eventually evolved into modern AI systems, which use large language models to allow for the output to be affected by the human language that is input. This distinction of AI models now being able to adjust their output is the driving force behind why AI is a topic of discussion in cancer care in 2025, Chen explained.
AI can currently help oncologists practicing in the community in a multitude of areas, including by gathering patient-specific data, quickly reviewing the existing published literature, and synthesizing payor information. AI-based tools have also displayed clinical utility in noting and tracking updates related to monitoring treatment-related adverse effects and treatment responses in patients.
What Is the Role of the Oncologist in the Development of AI Tools for Community Oncology Practice?
Chen transitioned his presentation to include a demonstration of how an AI-enabled chatbot can be used to support clinical decision-making in community practice. He noted that it is the responsibility of the individual who is inputting the information to check for “hallucinations,” which are responses generated by the AI based on word probabilities. These hallucinations represent a major drawback of AI in 2025, Chen said.
