AI4RDD：人工智能和罕见病诊断：改进记忆过程的建议

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-07-16 DOI:10.1016/j.imavis.2025.105658

Serena Lembo , Paola Barra , Luigi Di Biasi , Thierry Bouwmans , Genoveffa Tortora

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引用次数: 0

摘要

由于其固有的复杂性、有限的数据可用性以及对高技能医生的需求，诊断罕见和复杂疾病提出了重大挑战。传统的诊断过程使用分散的方法，患者经常咨询多个专家，并访问不同的医疗机构来确定他们的病情。这种传统的方法经常导致延迟或不准确的诊断。全世界有1万多种罕见疾病影响着3.5亿多人，对创新和有效诊断解决方案的需求迫切而关键。人工智能（AI）的进步为解决这些挑战提供了有希望的工具。临床决策支持系统（CDSS）和计算机辅助诊断系统（CAD）等人工智能驱动的系统促进了复杂的医疗数据处理，整合了包括成像和基因组学在内的各种数据集，并支持基于证据的治疗决策。这些技术有可能实现更早、更准确的诊断，减少不必要的测试，并提高整体医疗效率。本研究提出了一个基于人工智能的CAD工具框架，它可以导致分布式知识模型。该框架旨在提高罕见病的诊断准确性，并改善全球罕见病患者的预后。该框架强调实现合乎道德的人工智能，以实现更好的数据集成和专家协作。

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AI4RDD: Artificial Intelligence and Rare Disease Diagnosis: A proposal to improve the anamnesis process

Diagnosing rare and complex diseases presents significant challenges due to their inherent intricacies, limited data availability, and the need for highly skilled physicians. Traditional diagnostic processes use a decentralized approach in which patients often consult multiple specialists and visit various healthcare facilities to determine their condition. This conventional method frequently leads to delayed or inaccurate diagnoses. With over 10,000 rare diseases affecting more than 350 million people worldwide, the demand for innovative and effective diagnostic solutions is urgent and critical.

Artificial intelligence (AI) advancements present promising tools to tackle these challenges. AI-driven systems, such as Clinical Decision Support Systems (CDSS) and Computer-Aided Diagnosis Systems (CAD), facilitate complex medical data processing, integrating diverse datasets, including imaging and genomics, and supporting evidence-based treatment decisions. These technologies have the potential to enable earlier and more accurate diagnoses, reduce unnecessary tests, and enhance overall healthcare efficiency.

This study proposes a framework for an AI-based CAD tool that can lead to a Distributed Knowledge Model. This framework seeks to improve diagnostic precision and enhance global patient outcomes for rare diseases. This framework emphasizes ethical AI implementation for better data integration and expert collaboration.

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.