AI language models helping doctors write cancer team treatment reports

AI summary

From the trial's eligibility criteria. Machine-read from the registry text on this page, so it can be wrong. The registry text below and the trial team decide who can take part.

The trial is looking for junior doctors with 3 to 5 years of clinical experience. They will write tumor board (MDT) reports, with or without help from a large language model.

Age

25 to 33 years

Sex

All sexes can join.

Main reasons for not taking part

• Took part in the earlier diagnosis and treatment of any of the 20 cases in the study

The registry criteria, word for word

Check if you might fit

Answer for yourself. This is not a decision: only the study team can say whether you can take part.

• Are you between 25 and 33 years old?YesNo

What the trial wants to find out

Multidisciplinary teams (MDTs) represent the gold standard for personalized tumor treatment, but they are limited by medical resources and accessibility Limitation. Although large language models (LLMs) have shown promise in medical reasoning, their multidisciplinary practicality in pan-cancer MDTs has not been fully explored. In the early stage of this project, LLMs with high clinical application efficacy were identified through benchmark tests, and an open-label randomized controlled study (RCT) was conducted based on these LLMs. The research aims to explore whether AI-assisted assistance can enhance the accuracy and writing efficiency of MDT diagnosis and treatment reports. This study intends to prospectively collect the diagnosis and treatment information of 20 patients and MDT diagnosis and treatment information. It is planned to recruit 40 junior doctors. Doctors in the intervention group will use LLM to assist in the writing of MDT reports, while doctors in the control group will use traditional information retrieval methods for the writing of MDT reports. Three clinical experts ultimately used a standardized Likert scale to conduct comprehensive and multidisciplinary scoring of the MDT reports of the intervention group and the control group. This study quantitatively compared the diagnosis and treatment quality and efficiency of the MDT AI-assisted model and the traditional model to verify the application potential of large language models in assisting tumor diagnosis and treatment.

The 2 groups in the trial

• AI-MDTTreatment being tested

  This study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors.

  Treatment: LLM assists in MDT report writing

• Trad-MDTNo study treatment, for comparison

  The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions.