# AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis

## TL;DR

This paper introduces an AI-powered platform to improve lung cancer diagnosis by streamlining multidisciplinary team consultations and enhancing decision-making.

## Contribution

The AI-MDT platform integrates automation, decision support, and imaging analysis to improve efficiency and accuracy in lung cancer MDT workflows.

## Key findings

- The AI-MDT platform was used in 879 consultations, significantly increasing consultation volume and reducing expert time.
- AI-generated recommendations were used in 852 cases, showing strong clinical utility in diagnostic support.
- The platform enhanced data utilization and demonstrated potential for broader application in medical specialties.

## Abstract

Multidisciplinary team (MDT) consultations are crucial for managing pulmonary nodules, yet face challenges in efficiency, evidence-based decision support, and data utilization within the MDT process. We present an integrated artificial intelligence (AI)-MDT platform that serves as an assistive tool for lung cancer MDT workflows by incorporating AI across various processes.The aim of this study is to evaluate the clinical utility and preliminary efficacy of the AI-MDT platform.

The platform comprises three core modules: process automation, intelligent decision support, and diagnostic assistance. It integrates a real-time, evidence-based knowledge base powered by large language models and deep learning, with computer vision for automatic lesion detection and feature analysis. A web-based interface allows users to interact seamlessly with the AI-MDT platform.

Since its implementation in November 2023 at a tertiary Grade A hospital in China, the platform has been involved in 879 consultations, including 811 patients. AI-generated diagnostic recommendations were utilized 852 times, and decision-making support was used in 744 cases. The platform significantly increased consultation volume, reduced expert time, and enhanced data utilization compared to traditional MDT.

It offers clinicians tools to improve diagnostic quality and work efficiency, highlighting its significant clinical application value. These findings suggest that the proposed platform contributes to the emerging research on advances precision lung cancer management by integrating a continually updated evidence base and intelligent imaging methodologies, having potential implications for MDT processes across various medical specialties.