用可解释AI提升癌症诊断准确率与可信度
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models
- 结合XAI与深度学习，让模型决策过程透明可懂
- 提升诊断准确性，助力早期发现与个性化治疗
- 适合医疗AI研究者及临床医生参考应用
本研究提出一种基于可解释人工智能（XAI）与深度学习的癌症诊断新方法。2020年全球癌症导致近1000万例死亡，早期精准诊断至关重要。传统方法常受限于成本、准确率与效率。本研究开发的AI模型不仅实现高精度预测，还能清晰揭示其决策依据，解决深度学习模型的“黑箱”问题。通过引入XAI技术，增强模型可解释性与透明度，提升医护人员与患者信任度。模型利用神经网络分析大规模数据，识别癌症检测的关键模式。该方法有望革新医疗诊断，提升准确性、可及性与决策透明度，推动早期筛查与个体化治疗。尤其在资源匮乏地区，可能促进高质量诊断的普及，助力全球健康公平。其应用潜力还可扩展至其他医学决策场景，有望挽救全球数百万生命。
原文摘要 · Abstract (English)
This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate diagnosis is crucial. Traditional methods often face challenges in cost, accuracy, and efficiency. Our study develops an AI model that provides precise outcomes and clear insights into its decision-making process, addressing the "black box" problem of deep learning models. By employing XAI techniques, we enhance interpretability and transparency, building trust among healthcare professionals and patients. Our approach leverages neural networks to analyse extensive datasets, identifying patterns for cancer detection. This model has the potential to revolutionise diagnosis by improving accuracy, accessibility, and clarity in medical decision-making, possibly leading to earlier detection and more personalised treatment strategies. Furthermore, it could democratise access to high-quality diagnostics, particularly in resource-limited settings, contributing to global health equity. The model's applications extend beyond cancer diagnosis, potentially transforming various aspects of medical decision-making and saving millions of lives worldwide.
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