The precise figure is unknowable because only 3 to 10% of this fraud is ever detected. With more data in health care than ever before, what is the opportunity to use artificial intelligence (AI) and other advanced analytics techniques to improve fraud detection? That was the topic of a recent webinar featuring Prime Therapeutics, the pharmacy benefit manager that Fast Company named among the world's most innovative companies for 2020 for their use of SAS Detection and Investigation for Health Care to fight fraud, waste and abuse. SAS Medical Director Steve Kearney, PharmD, hosted the webinar. Prime Therapeutics integrates pharmacy and medical claims into an advanced analytic engine to identify cases for their investigators.

The healthcare industry is increasingly focusing on niche patient populations. Around half of FDA approvals in the past two years were for rare or orphan drugs that serve fewer than 200,000 patients in total in the US and 1 in 2,000 patients in Europe. By 2024, orphan drug sales are expected to capture one-fifth of worldwide prescription sales. However, finding these hard-to-reach patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?

Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."

Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."

The Food and Drug Administration (FDA) has cleared two additional Siemens Healthineers artificial intelligence-based software assistants in the AI-Rad Companion family. The AI-Rad Companion Prostate MR for Biopsy Support automatically segments the prostate on MRI images and enables radiologists to mark lesions, facilitating targeted prostate biopsies. "These new AI-Rad Companion applications for MR exams in the brain and prostate regions will help physicians manage their workloads and achieve a patient-focused decision-making process to increase efficiency and improve the quality of care," said Peter Shen, Vice President of Innovation and Digital Business at Siemens Healthineers North America. The AI-Rad Companion Brain MR for Morphometry Analysis supports brain volumetry, which involves measuring the volume of gray matter (nerve cells), white matter (nerve cell connections), and cerebrospinal fluid in various segments of the brain and comparing the results to normal volumes. In typical clinical presentation and when combined with independent confirmation, reduced volume may indicate Parkinson's disease, Alzheimer's disease, or other forms of dementia.¹

MRI scanners take time to carry out a scan. Clinicians spend up to an hour gathering sufficient data for a diagnostic MRI examination, which eats into a hospital's demanding schedule. To make the scanning process quicker for the patients, Facebook AI and NYU Langone Health announced a major research milestone that could significantly improve the patient experience, expand access to MRIs, and potentially enable new use-cases for MRIs. This study was published in the American Journal of Roentgenology. The radiologists who were tasked with reviewing AI-generated and traditional MRI images, could not tell which were created using the new method.

The No. 1 overarching hot topic at all the medical conferences over the past couple years has been artificial intelligence (AI). What was once science fiction or far-fetched research projects are now starting to gain U.S. Food and Drug Administration (FDA) market clearance. Some AI elements are already being used without clinicians knowing it, being integrated into the backend of cardiology imaging systems and IT reporting systems to help speed workflow. However, beyond the hype of AI, there are practical concerns, including the need for validation, clinical evidence showing AI helps patient care, and the payment system based on how medicine did things 20-30 years ago needs to change. "We have a huge gap between all this AI investment and how we actually take care of patients. We need to integrate it into our care, because if it is not part of how we take care of patients, this isn't going to work," explained John Rumsfeld, M.D., Ph.D., FACC, American College Cardiology (ACC) chief innovation officer, and professor of medicine at the University of Colorado School of Medicine.

A drug screen using machine learning has identified hundreds of potential drugs that could be used to treat COVID-19, researchers say. Researchers have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by SARS-CoV-2. The study was conducted at the University of California, Riverside, US. "There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Professor Anandasankar Ray, who led the research. "We have developed a drug discovery pipeline that identified several candidates… Existing US Food and Drug Administration (FDA)-approved drugs that target one or more human proteins important for viral entry and replication are currently high priority for repurposing as new COVID-19 drugs. The demand is high for additional drugs or small molecules that can interfere with both entry and replication of SARS-CoV-2 in the body. Our drug discovery pipeline can help."

Artificial intelligence (AI) is the most disruptive innovation of our lifetime. Its adoption has grown 60 percent in the last year, according to an April 2020 report by Narrative Science. The report's authors say the technology is having a "significant and imminent impact on everything from company strategy, to business operations, to job functions." So what are some of AI's implications in the new normal, one in which American entrepreneurs find themselves saving cash, working from home and wearing masks everywhere they go? Currently, for entrepreneurs, the most popular AI-powered solutions deal with predictive analytics (24 percent), machine learning (21 percent), language processing (14 percent) and voice recognition and response (14 percent), according to the same Narrative Science report.

"There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research. "We have developed a drug discovery pipeline that identified several candidates." The drug discovery pipeline is a type of computational strategy linked to artificial intelligence -- a computer algorithm that learns to predict activity through trial and error, improving over time. With no clear end in sight, the COVID-19 pandemic has disrupted lives, strained health care systems, and weakened economies. Efforts to repurpose drugs, such as Remdesivir, have achieved some success.