Oncologic treatment support via a dedicated mobile app: a prospective feasibility evaluation (OPTIMISE-1)

Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy

BACKGROUND

This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.

METHODS

Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc., San Francisco, CA, USA), Gemini (Google LLC, Mountain View, CA, USA), Copilot (Microsoft Corp., Redmond, WA, USA), and Claude (Anthropic PBC, San Francisco, CA, USA)] via the respective web interfaces. Responses were evaluated for readability using the Flesch Reading Ease Index. Five radiation oncologists assessed the responses for relevance, correctness, and completeness using a five-point Likert scale. Additionally, 35 prostate cancer patients evaluated the responses from ChatGPT‑4 for comprehensibility, accuracy, relevance, trustworthiness, and overall informativeness.

RESULTS

The Flesch Reading Ease Index indicated that the responses from all LLMs were relatively difficult to understand. All LLMs provided answers that clinicians found to be generally relevant and correct. The answers from ChatGPT‑4, ChatGPT-4o, and Claude AI were also found to be complete. However, we found significant differences between the performance of different LLMs regarding relevance and completeness. Some answers lacked detail or contained inaccuracies. Patients perceived the information as easy to understand and relevant, with most expressing confidence in the information and a willingness to use ChatGPT‑4 for future medical questions. ChatGPT-4's responses helped patients feel better informed, despite the initially standardized information provided.

CONCLUSION

Overall, LLMs show promise as a tool for patient education in prostate cancer radiotherapy. While improvements are needed in terms of accuracy and readability, positive feedback from clinicians and patients suggests that LLMs can enhance patient understanding and engagement. Further research is essential to fully realize the potential of artificial intelligence in patient education.

[A look into the neighboring discipline: eHealth in oncology]

Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.

Patient-reported symptoms before adjuvant locoregional radiotherapy for breast cancer: triple-negative histology impacts the symptom burden

BACKGROUND

Multimodal breast cancer treatment may cause side effects reflected in patient-reported outcomes and/or symptom scores at the time of treatment planning for adjuvant radiotherapy. In our department, all patients have been assessed with the Edmonton Symptom Assessment System (ESAS; a questionnaire addressing 11 major symptoms and wellbeing on a numeric scale of 0-10) at the time of treatment planning since 2016. In this study, we analyzed ESAS symptom severity before locoregional radiotherapy.

PATIENTS AND METHODS

Retrospective review of 132 patients treated between 2016 and 2021 (all comers in breast-conserving or post-mastectomy settings, different radiotherapy fractionations) was performed. All ESAS items and the ESAS point sum were analyzed to identify subgroups with higher symptom burden and thus need for additional care measures.

RESULTS

The biggest patient-reported issues were fatigue, pain, and sleep problems. Patients with triple negative breast cancer reported a higher symptom burden (mean 30 versus 20, p = 0.038). Patients assigned to adjuvant endocrine therapy had the lowest point sum (mean 18), followed by those on Her-2-targeting agents without chemotherapy (mean 19), those on chemotherapy with or without other drugs (mean 26), and those without systemic therapy (mean 41), p = 0.007. Those with pathologic complete response after neoadjuvant treatment had significantly lower anxiety scores (mean 0.7 versus 1.8, p = 0.03) and a trend towards lower depression scores, p = 0.09.

CONCLUSION

Different surgical strategies, age, and body mass index did not impact on ESAS scores, while the type of adjuvant systemic therapy did. The effect of previous neoadjuvant treatment and unfavorable tumor biology (triple negative) emerged as important factors associated with symptom burden, albeit in different domains. ESAS data may facilitate identification of patients who should be considered for additional supportive measures to alleviate specific symptoms.