Americans will generate more clinical grade biological data like daily vital signs in the next 5 years than has previously been recorded in the past 20 years. The data will be more accurate since it won't be one snapshot in time, but many snapshots in someone's daily life. While most clinical grade vital signs are collected and recorded in a healthcare setting like a clinic, hospital, or ER, there are a number of factors changing that quickly. The combination of AI based software & medical devices that have cleared the FDA, payor reimbursement, clinical adoption, and patient adoption are all coming together to bring RPM mainstream. This is impactful for a number of reasons.

In analyzing the news and media landscape, the report states that the nature of subscription services has changed, and will continue to evolve as consumers will be asked to pay for virtual fashion, experiences and games. The report cites subscription service platform Zuora that the media business has an average subscription dropout rate of 34 percent, the highest of any industry sector studied. It stresses that local newspapers are not just competing with the likes of The Washington Post or The New York Times, but every audience-funded business.

New treatments for Alzheimer's disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence based method to screen currently available medications as possible treatments for Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment - but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," explains Artem Sokolov, PhD, director of Informatics and Modeling at the Laboratory of Systems Pharmacology at HMS. "We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones."

AI has played a role in breaking some of the biggest international news stories of recent years. A key example is the'Panama Papers' exposé, where machine learning helped an international team of researchers to identify loan agreements in more than 13 million records that were leaked to the press. Journalists were able to'follow the money', exposing the practices of offshore tax havens and the businesses taking advantage of tax loopholes. Machine learning also played a valuable role in the Implant Files investigation. Here, it sifted through reports sent to the US Food and Drug Administration (FDA), and helped to uncover patient deaths potentially caused by faulty medical devices.

Software that uses artificial intelligence (AI) may help improve breast cancer diagnosis. QuantX, developed in Chicago, uses AI to analyze breast MRIs. Radiologists can use the technology to help assess if breast lesions are cancerous. Research shows the technology led to a 39% reduction in missed cancers, according to a clinical trial. Maryellen Giger, PhD, a professor of radiology at the University of Chicago, developed the technology, which the FDA cleared in 2017.

AI has the potential to revolutionize healthcare worldwide. In drug discovery, AI has already shown success. Sumitomo Dainippon Pharma and the UK-based AI company Exscientia developed DSP-1181 to treat obsessive compulsive disorder. In clinical trials for treatment of solid tumors, the clinical-stage, AI-powered biotech BERG's BPM31510 (ubidecarenone) has already been granted Orphan Drug Designation by the FDA to treat pancreatic cancer and epidermolysis bullosa, a rare skin disorder causing blistering. AI-led drug discovery for COVID-19 is also in the works.

With so many novel drug candidates for Alzheimer's disease failing in clinical development, researchers in the US have started using artificial intelligence (AI) to screen already-approved therapies for activity against the neurodegenerative disorder. A team based at Massachusetts General Hospital and Harvard Medical School has come up with an AI algorithm – called DRIAD (Drug Repurposing In Alzheimer's Disease) – that it hopes will not only find treatments but also tease out new therapeutic targets. The AI uses machine learning to measure what happens to human brain neural cells when treated with a drug, and could be "a more rapid and less expensive option" than clinical trials of novel therapeutics, according to the researchers. In the journal Nature Communications, Harvard informatics specialist Artem Sokolov and colleagues report that early studies with the platform based on 80 approved drugs suggest Eli Lilly's Olumiant (baricitinib) as a possible candidate for repurposing as an AD therapy. It's not the first time that AI has suggested a new role for Olumiant, which is approved as an arthritis drug.

Artificial intelligence (AI) is "adaptive," meaning that it continuously learns algorithms.

Could janus kinase (JAK) inhibitors like Eli Lilly's rheumatoid arthritis drug Olumiant be repurposed to treat Alzheimer's disease? Researchers at Harvard University and Massachusetts General Hospital have set out to find the answer to that question with a new clinical trial that was born from artificial intelligence. The researchers used a type of AI called machine learning to identify existing drugs that might be able to prevent neuronal death in Alzheimer's. The screen pulled up a list of 15 FDA-approved drugs as candidates for repurposing in Alzheimer's, and five of them were JAK inhibitors, they reported in the journal Nature Communications. JAK proteins fuel inflammation and have long been suspected to play a role in Alzheimer's.

Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We, therefore, built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.