Objectives To develop and evaluate machine learning models to detect suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for diagnostic screening and clinical management. Methods In this retrospective observational noninterventional study using administrative medical claims data from 1,463,089 patients, gradient-boosted decision trees were trained to detect likely NASH patients from an at-risk patient population with a history of obesity, type 2 diabetes mellitus (T2DM), metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (non-NAFL at-risk patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall and receiver operating characteristic curves (AUPRCs, AUROCs). Results The 6-month incidence of NASH in claims data was 1 per 1,437 at-risk patients and 1 per 2,127 non-NAFL at-risk patients. The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 - 0.011) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60x above NASH incidence. The model trained to detect NASH in non-NAFL patients had an AUPRC of 0.003 (95% CI 0.0029 - 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20x above NASH incidence. Conclusion The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of probable NASH patients for diagnostic testing and disease management.

After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time.

A new machine learning (ML) platform can identify patients with colorectal cancer and helps predict their disease severity and survival, finds a new study. The non-invasive method adds to recent advances in technologies that analyse circulating tumour DNA (ctDNA) and could help spot colorectal cancers in at-risk patients at earlier stages. Like many other malignancies, colorectal cancers are most treatable if they are detected before they have metastasized to other tissues. Colonoscopies are the'gold standard' for diagnosis, but they are uncomfortable and invasive and can lead to complications, which leaves patients less willing to undergo screening. For the study, published in the journal Science Translational Medicine, lead researcher Huiyan Luo from University Cancer Center in China and colleagues leveraged machine learning techniques to develop a less invasive diagnostic method that can detect colorectal cancer in at-risk patients. Their technology works by screening for methylation markers, which are DNA modifications that are frequently found in tumors.

The healthcare IT world is buzzing about the growing role of artificial intelligence (AI). This emerging, promising technology may soon help physicians diagnose and treat their patients faster and more accurately using complex algorithms and datasets–and in some cases, can even diagnose patients faster than human physicians. This future is not far away, either. Here are three ways AI will change the patient experience in the near future. AI in medical imaging has existed since the 1980s.

After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time.

A life-saving AI system that can identify the signs of malnutrition from a single photo of someone has been developed by a non-profit organisation. This system, called MERON (Method for Extremely Rapid Observation of Nutritional status) is still a prototype, but was 78 per cent accurate on the adults it was tested on. The technology reduces the need for lots of equipment and specialists in the field, making it easier to identify the symptoms of malnutrition, developers claim. By spotting the signs sooner, treatment can also be administered before the condition becomes critical. A non-profit organisation has developed technology which uses AI to identify the signs of malnutrition from a single photo of a person.

Join us to use cutting edge machine learning to unbreak healthcare in the US. In the US, physicians face huge informational challenges – from dealing with mountains of formulaic email to wrestling with arcane insurance rules to finding at-risk patients in their large client pools. Athenahealth's Data Science group is using advanced machine learning and AI to develop a new generation of smart tools that can help physicians by reducing their paperwork, finding at-risk patients, providing key information at the right time, and overall allowing physicians to focus on what's important: spending time with patients. We're seeking experienced data scientists who love machine learning and complex data and who care about making a positive impact on the world by fielding real ML-driven systems. Positions are available at multiple levels of seniority.

Poor adherence is a widespread medical problem, which has poor health outcomes and inflates healthcare costs. According to the U.S. National Library of Medicine, 75 percent of Americans face trouble taking medicine as instructed by their doctors. Israeli personalized medication management platform, Medisafe, wants to change this using artificial intelligence (AI). The start-up uses AI and machine learning on its medication adherence platform. It passively collects data from patients, such as medications prescribed, health measurements and uses self-learning algorithms, which can help a patient adhere to instructed medication better.