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Special Session: Artificial Intelligence in Medicine

机译：特别会议：医学人工智能

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In conventional settings, diagnosis of diseases and further medical and surgical treatment choices are mostly subjective, based on limited assessment of patient phenotypes. In the era of petabytes of clinical data and detailed patient information, from genomics to histological or volumetric images, gathered over entire lifespans, AI and big data analysis are instrumental in providing decision support and to understanding patient treatments.In this session, we discussed state-of-the-art AI methods to analyze multimodal patient data, understand their responses to medication and treatments, and to predict the risk of diseases prior to onset. Our focus was on topics related to methods for clinical diagnosis and outcome prediction, identification of increased-risk patient categories, ethics and security of healthcare data used by AI, and methods for computational pathology.From the large number of submitted papers, and together with our dedicated reviewers, we selected the four most interesting contributions for oral presentation. Our speakers reported advances in diagnosing breast cancer, clinical text mining, and the use of EEG for predicting certain disorders. In addition to the accepted papers, Dr. Shadi Albarqouni, senior research scientist at the Technische Universitat Miinchen, was invited to present his work on deep learning for medical imaging.
机译：在常规情况下，基于对患者表型的有限评估，疾病的诊断以及进一步的医学和外科治疗选择大多是主观的。在数十亿字节的临床数据和详细的患者信息时代，从基因组学到组织学或体积图像，贯穿整个生命周期收集，人工智能和大数据分析在提供决策支持和理解患者治疗方面发挥了重要作用。 在本次会议中，我们讨论了最新的AI方法，以分析多模式患者数据，了解他们对药物和治疗的反应以及预测发病前的疾病风险。我们的重点是与临床诊断和结果预测方法，高风险患者类别的识别，人工智能使用的医疗数据的道德和安全性以及计算病理学方法有关的主题。 从大量提交的论文中，以及我们专门的审稿人中，我们选择了四个最有趣的口头陈述文稿。我们的演讲者报告了在诊断乳腺癌，临床文本挖掘以及使用EEG预测某些疾病方面的进展。除了被接受的论文之外，慕尼黑工业大学的高级研究科学家Shadi Albarqouni博士还应邀介绍了他在医学成像深度学习方面的工作。

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