Ask Chat GPT about Your Radiation Oncology Treatment

Cancer patients about to undergo radiation oncology treatment have lots of questions. Could ChatGPT be the best way to get answers?

A new Northwestern Medicine study tested a specially designed ChatGPT to see if it could successfully provide answers to patients' common questions about radiation oncology. Patients may be too overwhelmed to address all their concerns during a clinical visit or forget what the physician told them.

The study showed ChatGPT's responses to 115 common radiation oncology patient questions was on par or exceeded answers from professional societies in terms of accuracy, completeness and conciseness in a majority of cases.

"The goal of this project is to empower patients," said first author Dr. Amulya Yalamanchili, a Northwestern Medicine radiation oncology resident. "This is a really technical field that can be hard to understand. All this information can be overwhelming to patients. If they have cancer in a sensitive area, they may not feel comfortable asking what their life will look like long term. The hope is patients can educate themselves with ChatGPT before and after they see a physician."

The AI may also reduce clinician workload and potentially reduce burnout, Yalamanchili said, as the incidence of newly diagnosed cancer cases climbs and the demands on provider time continue to increase.

The paper will be published April 2 in JAMA Network Open.

More than 60% of cancer patients - 500,000 per year - require some form of radiation oncology treatment, according to the National Cancer Institute.

While ChatGPT performed well overall, several responses were concerning, the scientists said. One was that it used language at a college reading level, which is more complex than professional society websites. The others were that it omitted details about a specialized technology for brain tumors and failed to mention the tiny tattoos sometimes used to position the patient for radiation treatments.

The concerns are being addressed by the Northwestern scientists, who stress that this technology needs ongoing refinement and training in specialized medical domains.

"The study highlights the potential of AI-driven technologies, such as ChatGPT, to provide accurate and comprehensive responses to patient queries in the field of radiation oncology, but we need to make these AI chatbot responses easier for people to understand," said corresponding author Peng (Troy) Teo, an instructor of radiation oncology at Northwestern University Feinberg School of Medicine. "These AI chatbots could serve as valuable tools for assisting health care providers in answering patient questions, particularly in contexts where access to expert advice may be limited."

Next, Northwestern scientists are developing and testing an in-house app using Generative AI to answer patient questions about radiation oncology treatment.

"We're exploring how these advanced AI chat systems can do more than just deliver quick, accurate and personalized information about radiation oncology treatment," said Dr. Bharat Mittal, chair of radiation oncology at Feinberg and a co-author of the paper. "We're also examining how they can be integrated into daily routines of patient care, ensuring care is more efficient and precise."

The title of the article is "Quality of Large Language Model Responses to Radiation Oncology Patient Care Questions."

Yalamanchili A, Sengupta B, Song J, Lim S, Thomas TO, Mittal BB, Abazeed ME, Teo PT.
Quality of Large Language Model Responses to Radiation Oncology Patient Care Questions.
JAMA Netw Open. 2024 Apr 1;7(4):e244630. doi: 10.1001/jamanetworkopen.2024.4630

Most Popular Now

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

Philips Foundation 2024 Annual Report: E…

Marking its tenth anniversary, Philips Foundation released its 2024 Annual Report, highlighting a year in which the Philips Foundation helped provide access to quality healthcare for 46.5 million people around...

Scientists Argue for More FDA Oversight …

An agile, transparent, and ethics-driven oversight system is needed for the U.S. Food and Drug Administration (FDA) to balance innovation with patient safety when it comes to artificial intelligence-driven medical...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

Start-ups in the Spotlight at MEDICA 202…

17 - 20 November 2025, Düsseldorf, Germany. MEDICA, the leading international trade fair and platform for healthcare innovations, will once again confirm its position as the world's number one hotspot for...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...