AI-assisted clinical summary and treatment planning for cancer care: A comparative study of human vs. AI-based approaches.
Authors: Po-Hsuan Cameron Cameron Chen, Ji-Jung Jung, Yoona Kim, Minjung Lee, Rodrigo Sanchez-Bayona, Paul J Bröckelmann, Robert Anton Olson, Denise Bernhardt, Christopher Goodman, Matthew Cecchini, Michael Yan, Houda Bahig, Sherman Lin, Joseph Y. Cheng, Petros Giannikopoulos, William R. Polkinghorn, David A Palma, Han-Byoel Lee
Journal: Journal of Clinical Oncology (2024)

1523 Background: Understanding a patient's clinical narrative, timeline, and history is critical for accurate treatment decision-making. However, reviewing and summarizing complex records is time-consuming and error-prone. Recent advancements in artificial intelligence (AI), specifically large language models (LLM), offer paths to improve quality and efficiency. Methods: A study was conducted on 50 breast cancer cases from an academic medical institution, utilizing all medical records—clinic, pathology, and radiology reports—up until the point of the initial treatment decision. All cases were processed using three different approaches: AI-assisted; full-AI; and human-only. In the AI-assisted method, two oncology physician assistants (PAs) revised AI-generated summaries to create clinical summaries. The full-AI method had AI independently produce clinical summaries, while the human-only method had the PAs compile summaries without AI. Eight board-certified international oncology specialists blindly evaluated summaries for faithfulness, completeness, and succinctness using a 3-point scale, ranked their preferences, and tried to predict which summaries were full-AI. Rankings were assessed using a Friedman test followed by a Wilcoxon signed-rank test, and full-AI prediction was assessed using a two-sided one-sample binomial test. After summarization, a distinct AI system with access to clinical guidelines provided treatment plans. These plans were then evaluated by a board-certified oncologist with access to the original treatment decision. Results: The study found specialists favored AI-assisted, followed by full-AI, and then human-only summaries, with average ranks of 1.73, 1.93, 2.34 respectively (lower is better, p&lt;0.001). The difference between full-AI and AI-assisted was not significant (p=0.11). Evaluation scores (mean±95%CI, higher is better) showed AI-assisted, full-AI, and human-only scored 2.35±0.13, 2.14±0.14, 2.17±0.14 for faithfulness; 2.28±0.12, 2.01±0.12, 1.93±0.14 for completeness; and 2.33±0.12, 2.21±0.12, 1.99±0.13 for succinctness. The average summarization time was 19.71, 1.17, 26.03 minutes. Full-AI identification accuracy was 0.28 (not different from chance 0.33, p=0.46). With AI-assisted summaries, the treatment plans were accurate in 45 cases (90%) and partially accurate in 5 cases (10%). In the 5 partially accurate cases, the system was accurate with the provided input data, but there were inaccuracies with the input data, including incorrect formats or missing data. Conclusions: Incorporating LLMs into the creation of medical summaries has shown improvements in both quality and efficiency, achieving up to 22.2x speed up with full-AI, indicating that AI-assisted summarization tools can potentially enhance care quality. AI-assisted summaries yield accurate treatment plans when the input data is accurate.