AI Article Synopsis

  • The study explores the use of Large Language Models (LLMs) to enhance decision-making in urological cancer treatment, aiming to compare their recommendations with those of traditional tumor boards.
  • It validates two scoring scales, the System Causability Scale (SCS) and its modified version (mSCS), showing they have strong reliability and internal consistency for future trials.
  • The upcoming CONCORDIA trial will formally test LLM recommendations against multidisciplinary tumor board recommendations across 110 urological cancer scenarios.

Article Abstract

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This preparatory study establishes a robust methodological foundation for the forthcoming CONCORDIA trial, including the validation of the System Causability Scale (SCS) and its modified version (mSCS), as well as the selection of LLMs for urological cancer treatment recommendations based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen's K > 0.74), and internal consistency (all Cronbach's Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability ( < 0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593082PMC
http://dx.doi.org/10.3390/curroncol31110520DOI Listing

Publication Analysis

Top Keywords

urological cancer
16
validation system
8
system causability
8
causability scale
8
treatment recommendations
8
cancer scenarios
8
internal consistency
8
concordia study
8
mtb recommendations
8
recommendations
7

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!