The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
arXiv:2605.26346v1 Announce Type: new Abstract: Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-trial identification system integrated into routine radiation oncology practice. Design: Mixed-methods evaluation using a cross-sectional, anonymous clinician survey administered after 1 month of system deployment. Exposure: Daily automated delivery of physician-specific email summaries generated using RadOnc-GPT, including patient schedules, concise EHR-derived clinical-status summaries, and automa
The proliferation of advanced LLMs and increasing demand for efficiency in healthcare are driving the development and early clinical evaluation of automated summarization and identification systems.
This development demonstrates a tangible application of AI in clinical settings, potentially transforming medical workflows and improving physician efficiency and patient care through automation.
Clinical documentation and trial identification processes in radiation oncology can now be partially automated by AI, reducing manual workload and potentially accelerating patient access to trials.
- · Healthcare providers
- · AI software developers
- · Patients
- · Medical research institutions
- · Legacy medical software vendors
- · Manual data entry roles
Physicians will spend less time on administrative tasks and more on direct patient care.
Accelerated clinical trial recruitment could expedite drug and treatment development.
Wider adoption of AI in healthcare could lead to new regulatory frameworks and increased scrutiny of AI ethics in medicine.
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Read at arXiv cs.CL