Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
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.
This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.
Artificial intelligence is beginning to be applied in the medical setting and has potential to improve workflows and errors, impacting patients and clinicians alike.
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.
Computer scientists must identify sources of bias, de-bias training data and develop artificial-intelligence algorithms that are robust to skews in the data, argue James Zou and Londa Schiebinger.
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.