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There are now few areas of our lives that artificial intelligence (AI) does not touch. Not only is medicine no exception, but the potential for digital transformation of health care is especially striking.
Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.
The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for digital pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
This Perspective examines key ethical challenges of ingestible electronic sensors, which are related to patients, physicians, and society more generally, and provides a comparative analysis of legal regulation of the sensors in the US and Europe.
The safety and security of medical devices driven by software, the software-development processes, and the need for data collection and privacy, all offer challenges and opportunities for device regulation and clinical care.