Last year the United States Food and Drug Administration (FDA) cleared a total of 12 AI tools that use machine learning for health (ML4H) algorithms to inform medical diagnosis and treatment for patients. The tools are now allowed to be marketed, with millions of potential users in the US alone.Because ML4H tools directly affect human health, their development from experiments in labs to deployment in hospitals progresses under heavy scrutiny. A critical component of this process is reproducibility. A team of researchers from MIT, University of Toronto, New York University, and Evidation Health have proposed a number of "recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward" in their new paper Reproducibility in Machine Learning for Health. Just as boxers show their strength in the ring by getting up again after being knocked to the canvas, researchers test their strength in the arena of science by ensuring their work's reproducibility.

Butterfly Network is reinventing medical imaging and championing a new era of healthcare by creating the first ever pocket-sized, whole-body ultrasound device – the Butterfly iQ. This breakthrough technology has reduced the cost of the traditional ultrasound system by miniaturizing it onto a single semiconductor silicon chip. Our mission is to democratize healthcare by making medical imaging accessible to everyone around the world. Since inception, Butterfly has raised over $375 million. The iQ is FDA-cleared and is being sold in hospitals and clinics around the globe.

The invasive and expensive diagnosis process of bladder cancer, which is one of the most common and aggressive cancers in the United States, may be soon helped by a novel non-invasive diagnostic method thanks to advances in machine learning research at the San Diego Supercomputer Center (SDSC), Moores Cancer Center, and CureMatch Incorporated. Research scientists Igor Tsigelny and Valentina Kouznetsova have been working on the development of a machine-learning (ML) model that looks at a patient's metabolites and their chemical descriptors. The model accurately classifies the stages of bladder cancer in a patient, according to the researchers. Tsigelny is the lead author on a recently published study in the Metabolomics journal called'Recognition of Early and Late Stages of Bladder Cancer using Metabolites and Machine Learning'. When a patient experiences early symptoms of bladder cancer (e.g., blood in urine, pain during urination, etc.), the current method of diagnosis is often a painful, invasive series of tests.

CHICAGO – PhysIQ, a leader in applying artificial intelligence to wearable sensor data, today announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for their algorithm to continuously determine respiration rate in ambulatory patients. This clearance adds to their expanding portfolio of FDA-cleared cloud-based analytics, which also include QRS detection, heart rate, heart rate variability, atrial fibrillation detection, and their personalized physiology change detection analytic. The latest clearance advances physIQ's strategy to offer a deep portfolio of FDA-cleared analytics that can be applied to wearable sensor data. To enable this, physIQ's platform collects raw telemetry from the device and uploads it to the cloud where FDA-cleared analytics use the raw biosignals to produce vital signs. With this approach physIQ is able to provide vital sign analytics that benefit from the superior computing power of the cloud and fuel the higher-level analytics that further characterize dimensions of human physiology.

A Google research team has recently created an artificial intelligence (AI) system that can detect 26 different skin diseases with the same accuracy as a licensed dermatologist. This deep learning technology evaluates images and metadata, such as self-reported symptoms and demographic information, to generate a ranked list of possible diagnoses just as a trained professional would. The Google team's findings were covered in a paper titled "A deep learning system for differential diagnosis of skin diseases" and in a blog post penned by, Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health. With nearly 2 billion patients having some form of skin condition globally and many areas lacking dermatologists, patients must often take such concerns up with their primary care physicians. Research has shown that while dermatologists diagnose these skin conditions with accuracies between 77-96%, the general practitioner does so with only 24-70% accuracy.

And humans need health screenings, especially for the heart. That's because heart disease is the leading cause of death worldwide. With deep learning, heart disease diagnosis is becoming easier and more accessible -- which in turn can improve treatment and patient outcomes. Echocardiograms -- ultrasound tests that generate images of the heart -- are used to detect and manage heart disease cases. An echo, as it's commonly called, is also used as an assessment tool for specific populations, such as chemotherapy patients, because of their increased risk of heart failure.

As artificial intelligence becomes a standard laboratory tool, scientists are quickly discovering both the promise and perils of algorithmically driven research. Artificial intelligence (AI) is cropping up everywhere these days, according to major news sources that are themselves increasingly driven by computer algorithms. Marketers use AI to target advertisements, engineers use it to anticipate device failures, and AI-driven social media platforms wield outsize influence on everything from fashion to politics. While all types of AI--also called machine learning--entail programming a computer to learn from examples and make inferences, practitioners distinguish different forms of it. Within the broader field of AI, a subset of strategies employ artificial neural networks. These mimic biological brains, with elements of a program connecting to each other like neurons.

Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318) is pleased to announce Ping An Global Voyager Fund is leading an investment of US$15 Million in Riverain Technologies, a leading provider of clinical artificial intelligence software used to efficiently detect lung disease at its earliest stages. Riverain Technologies markets advanced artificial intelligence imaging software used by leading hospitals around the world. The software significantly improves a clinician's ability to accurately and efficiently detect cancer and other cell anomalies in thoracic CT and X-ray images. The company's suite of patented ClearReadTM software tools are FDA-cleared, deployable in the clinic or in the cloud, and powered by the most advanced artificial intelligence and machine learning methods available to the medical imaging market. Its products are relied upon by leading healthcare institutions, including Duke University, Mayo Clinic, University of Chicago, University of Michigan, and Veterans Affairs hospitals.

You are in a black sedan parked in a dimly-lit alley at night. In the passenger seat a female, dressed in white top and jeans, asks if you want to share some meth. She lights up and smoke fills the interior. The scenario may sound real enough but it is an immersive virtual reality (VR) experience designed to determine how prone the participant is to drug use by tracking their pulse, brainwaves and the electrical conductance of the skin. Using artificial intelligence that combines the responsive patterns from over 10,000 addiction cases, the system generates a drug craving score, according to Li Dai, founder and chief executive of Beijing-based start-up WonderLab, developer of the technology. The company is working with rehabilitation centers in more than 10 Chinese provinces and municipalities including Shandong, Sichuan, Yunnan, Beijing and Chongqing, to apply the AI-enabled assessment as a follow-up to addiction treatment.

A person receives a test for diabetes during Care Harbor LA free medical clinic in Los Angeles, California September 11, 2014. DreaMed Diabetes, the Petah Tikva-based developer of personalized diabetes management solutions, has received US Food and Drug Administration (FDA) clearance for its artificial intelligence-powered insulin recommendations technology. The company's AI-based insulin dosing decision-support software, DreaMed Advisor Pro, aims to assist people with Type 1 diabetes (T1D) using insulin-pump therapy with continuous glucose sensors or blood glucose meters.