The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmarks are essential for the development of intelligent systems. We present Personal Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a holistic suite of benchmarks for machine learning across different Healthcare settings - talk therapy, diet coaching, emergency care, intensive care, obstetric sonography - as well as different learning settings, such as action anticipation, timeseries modeling, insight mining, language modeling, computer vision, reinforcement learning and program synthesis

翻译：机器学习在医疗健康领域的应用有潜力改善患者预后，同时扩大医疗服务的覆盖范围并降低其成本。其他应用领域的发展历史表明，强大的基准测试对于智能系统的开发至关重要。我们提出了个人健康接口利用人机自然交互（PhilHumans），这是一个涵盖不同医疗场景（包括谈话疗法、饮食指导、急救护理、重症监护、产科超声检查）以及不同学习场景（如动作预测、时间序列建模、洞察挖掘、语言建模、计算机视觉、强化学习和程序合成）的综合性机器学习基准测试套件。