KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan-Meier Plots

2025-09-14

AI Summary

Current Kaplan–Meier (KM) plot digitization for individual patient data (IPD) reconstruction relies on manual intervention, resulting in substantial human error and poor scalability. To address this, we propose the first end-to-end automated IPD reconstruction framework, integrating multimodal image preprocessing, GPT-5–driven vision–language joint reasoning, and an iterative survival-data inversion algorithm. We further develop a zero-code web platform augmented with an AI-powered interactive assistant. Evaluated on both synthetic and real-world KM plots, our method achieves high reconstruction accuracy—median absolute error <2%—enabling robust downstream analyses. It has been successfully deployed in a meta-analysis of gastric cancer immunotherapy trials and biomarker-defined subgroup studies. This framework significantly enhances the accuracy, reproducibility, and scalability of evidence synthesis in clinical oncology and survival analysis.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Reconstructing individual patient data (IPD) from Kaplan-Meier (KM) plots provides valuable insights for evidence synthesis in clinical research. However, existing approaches often rely on manual digitization, which is error-prone and lacks scalability. To address these limitations, we develop KM-GPT, the first fully automated, AI-powered pipeline for reconstructing IPD directly from KM plots with high accuracy, robustness, and reproducibility. KM-GPT integrates advanced image preprocessing, multi-modal reasoning powered by GPT-5, and iterative reconstruction algorithms to generate high-quality IPD without manual input or intervention. Its hybrid reasoning architecture automates the conversion of unstructured information into structured data flows and validates data extraction from complex KM plots. To improve accessibility, KM-GPT is equipped with a user-friendly web interface and an integrated AI assistant, enabling researchers to reconstruct IPD without requiring programming expertise. KM-GPT was rigorously evaluated on synthetic and real-world datasets, consistently demonstrating superior accuracy. To illustrate its utility, we applied KM-GPT to a meta-analysis of gastric cancer immunotherapy trials, reconstructing IPD to facilitate evidence synthesis and biomarker-based subgroup analyses. By automating traditionally manual processes and providing a scalable, web-based solution, KM-GPT transforms clinical research by leveraging reconstructed IPD to enable more informed downstream analyses, supporting evidence-based decision-making.

Problem

• Automating reconstruction of individual patient data from Kaplan-Meier plots
• Replacing error-prone manual digitization with AI-powered scalable pipeline
• Enabling evidence synthesis without requiring programming expertise from researchers

Innovation

• Automated AI pipeline for Kaplan-Meier plot reconstruction
• Integrates image preprocessing, GPT-5 reasoning, and algorithms
• User-friendly web interface enabling IPD extraction without coding