A clinical-grade artificial intelligence model for pathology developed by researchers at Memorial Sloan Kettering Cancer Center in New York City can detect cancer in tissue slides with near-perfect accuracy.

If society is to comfortably adapt to the ongoing artificial intelligence-spurred global transformation, it is imperative that the field of AI ethics continues to expand, according to a recent op-ed written in part by former Secretary of State Henry Kissinger, PhD, and former Google CEO Eric Schmidt, PhD.

Since Beth Israel Deaconess Medical Center in Boston launched the MIMIC compendium of de-identified medical records in 2009, the database has proved essential to the advancement of artificial intelligence research in medicine, STAT reports.

The Department of Veterans Affairs announced July 10 the appointment of Gil Alterovitz, PhD, as its first director of artificial intelligence, a position that will be based in the VA's Office of Research and Development.

Here are six recent studies and product launches that incorporate machine learning into clinical settings:

As artificial intelligence increasingly transforms industries and society at large, it is crucial to consider how that transformation will affect human reasoning and decision-making, according to former Secretary of State Henry Kissinger, PhD, former Google CEO Eric Schmidt, PhD, and Daniel Huttenlocher, PhD, dean of the MIT Schwarzman College of Computing.

Researchers from Michigan Medicine in Ann Arbor have been granted access to San Francisco-based Atomwise's artificial intelligence software to accelerate their drug discovery projects.

Healthcare organizations have a tremendous opportunity to harness artificial intelligence in an area that may not seem flashy but is important nonetheless: revenue cycle management.

Machine learning analysis of patient data was able to predict the severity of malaria outcomes and the progression of the disease with significantly greater success than previous approaches, according to the results of a study published July 10 in npj Digital Medicine.

The Partnership for Artificial Intelligence, Telemedicine and Robotics in Healthcare (PATH) released a new set of ethical guidelines for the development and implementation of AI in healthcare.

Geisinger has tapped Medial EarlySign, an Israeli healthcare software company, to help the Danville, Pa.-based health system develop and deploy a suite of machine learning-powered digital solutions to identify patients at risk of high-burden diseases.

GlaxoSmithKline has hired Kim Branson, PhD, to serve as the British pharmaceutical company's global head of artificial intelligence and machine learning, Bloomberg reports.

A deep learning algorithm determined whether a thyroid nodule required a biopsy with a level of accuracy equal to or, in some cases, better than that of trained radiologists, according to a study published July 9 in Radiology.

Waystar, a Louisville, Ky.-based revenue cycle technology provider, has acquired Digitize.AI, a Charlotte, N.C.-based tech firm using artificial intelligence to make the prior authorization process faster and more efficient.

GNS Healthcare, a Cambridge, Mass.-based startup using artificial intelligence to develop precision medicine solutions, announced on July 9 the close of a $23 million funding round led by Cigna Ventures.

Though many claims about artificial intelligence's ability to upend healthcare, business processes and, in fact, all of society have been greatly exaggerated, there is no denying that the technology does indeed hold huge potential for transformation.

Adopting a data-driven model may have greatly improved the decision-making abilities of many organizations, but evolving toward a decision-making process powered by artificial intelligence will generate even more success, according to the Harvard Business Review.

Researchers used a machine learning algorithm powered by a massive network of ordinary smartphones to identify and map out potentially cancer-beating molecules in various foods.

Machine learning analysis of clinical notes in the electronic health records of inpatients at psychiatric treatment centers was able to predict with significant accuracy the patients' risk of violence within four weeks of admission, according to a study published July 3 in JAMA Network Open.

A prediction model using machine learning to analyze EHR data showed a marked improvement over previous models to predict a patient's risk of contracting HIV within three years, according to a study published July 5 in The Lancet HIV.