Language models analyse breast cancer treatment side effects
A new study examines the ability of large language models (LLMs) to identify side effects of breast cancer radiotherapy, focusing on reliability in clinical contexts.
What happened?
Researchers have developed a framework to stress-test seven instruction-tuned large language models (LLMs) regarding their ability to identify side effects of breast cancer radiotherapy. The study used 21 patient profiles with the radiotherapy regimen as the only variable to evaluate model performance under several prompting regimes. The focus was on assessing the reliability of the models in oncological contexts, specifically for informing patients about potential side effects.
Key facts
| Publikationsdatum | 22 maj 2024 |
|---|---|
| Antal patientprofiler | 21 |
| Antal testade LLM | 7 |
| Fokusområde | Biverkningar av strålbehandling vid bröstcancer |
”Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where clinicians must clearly and comprehensively convey potential treatment toxicities.”
”We present a deployment-oriented stress-testing framework for evaluating LLM-generated radiation side effect lists in breast cancer treatment and survivorship care.”
Why it matters
The task of accurately communicating side effects of cancer treatments is critical, particularly for informed consent, but is complicated by knowledge gaps and fragmented electronic health record systems. LLMs have the potential to assist in this, but their reliability within oncological follow-up care has been poorly explored. The study aims to fill this knowledge gap by evaluating their practical utility for clinicians and patients.
Who is affected?
The study primarily affects oncologists, radiotherapists, and other healthcare professionals who communicate treatment side effects to breast cancer patients. Developers of AI systems for healthcare are also affected, as the results point to modelled limitations and potentials. Breast cancer patients, as recipients of information, are the ultimate stakeholder group.
What else you should know
The study was published on 22 May 2024 on arXiv and is a preprint, meaning it has not yet undergone peer review. This is an important aspect to consider when interpreting the results.
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