AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment

2025-03-12

AI Summary

This study addresses the critical challenge of insufficient data readiness hindering the clinical deployment of AI-based decision support systems (CDSS) for skin cancer. Methodologically, it integrates a systematic literature review, multi-source information system data quality assessment, clinician-led workshops, and intelligent extraction and analysis of unstructured pathology text. It is the first to systematically define and empirically validate 12 essential skin cancer decision-critical data elements, quantifying their missingness rates and noise levels within electronic health records—revealing unstructured pathology reports as the primary data bottleneck. The contributions include: (1) establishing a reusable, domain-specific data readiness evaluation framework; (2) identifying and prioritizing actionable data governance interventions; and (3) providing methodological guidance and empirical evidence to bridge the gap between algorithmic validation and real-world clinical AI implementation in oncology settings.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital M""unster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.

Problem

• Evaluating data readiness for AI-based skin cancer treatment support.
• Assessing data quality, availability, and extractability for oncology AI applications.
• Identifying challenges in integrating AI into clinical decision-making for skin cancer.

Innovation

• AI-based Clinical Decision Support System
• Data readiness assessment methodology
• Integration of AI in oncology decision-making