When MIT mathematician Jim Simmons founded Renaissance Technologies in 1988, it was unfathomable that computers driven by algorithms might outperform top Wall Street fund managers. Today, after a more than thirty year average return of 66% per annum, the Renaissance model of applying machine learning techniques to invest in a manner that is automated, dispassionate and performed with minimal human intervention is widely accepted. Quantitative finance, as it is dubbed, has profoundly reshaped the financial industry. In the past, pharma has expressed a similar skepticism around the adoption of artificial intelligence and machine learning to perform tasks such as molecular design, discovery and clinical trials. After all, lives are at stake and Mark Zuckerberg's idea that success requires startups to "move fast and break things" is the antithesis of the industry's guiding principle to "do no harm."
Late last year, Stanford University researcher Amit Kaushal and a collaborator noticed something striking while sifting through the scientific literature on artificial intelligence systems designed to make a diagnosis by analyzing medical images. "It became apparent that all the datasets [being used to train those algorithms] just seemed to be coming from the same sorts of places: the Stanfords and UCSFs and Mass Generals," Kaushal said. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.
Since February of last year, tens of thousands of patients hospitalized at one of Minnesota's largest health systems have had their discharge planning decisions informed with help from an artificial intelligence model. But few if any of those patients has any idea about the AI involved in their care. That's because frontline clinicians at M Health Fairview generally don't mention the AI whirring behind the scenes in their conversations with patients. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.
If you've ever visited a doctor, you may find yourself receiving more ads for drugs in coming years. Advertising by Big Pharma directly to consumers is a small portion of the total online advertising market but may increase as new advertising tools, some of them using machine learning, are employed by the drug companies. "Pharma is about 18% of national GPD and only 3% of digital advertising, that's pretty astonishing," Christopher Paquette, CEO of New York-based DeepIntent Technologies, told ZDNet in a telephone interview. DeepIntent, founded just over four years ago, is part of publicly traded advertising technology firm Propel Media. "There is a $20 billion opportunity to unlock this digital advertising," said Paquette.
Using Botox to enhance your cheekbones or alter your appearance is all the rage, thanks to the Kardashians and Hadids of the world. One doctor is trying to bring Botox back to its wrinkle-fighting basics -- with the help of artificial intelligence. Peachy is a new studio in New York City, opening Wednesday, that uses A.I. to chart a Botox-based treatment plan for wrinkle prevention. Based on the unique muscle stress that smiling, frowning, concentrating, or expressing any emotion puts on your face, Peachy says its algorithm suggests the best places, amounts, and time frames for injecting Botox to keep temporary lines from becoming intractable wrinkles later in life. The manual tweaks physicians make to treatment plans as well as patient progress will work to improve the algorithm over time.
Artificial intelligence-focused health care companies raised nearly $1 billion in funding in the first quarter of 2020, according to a new report from data analytics firm CB Insights, reflecting a growing trend in health tech: As much of the world braces for a probable pandemic-era recession, some health startups are nailing crucial, if eleventh-hour, funding. But it was a welcome uptick from the final quarter of last year, when funding dipped for the first time all year. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.
ResoluteAI, the Connect to Discover company, announced the addition of a News dataset to their Foundation search platform for scientific content. In partnership with FinTech Studios, the leading AI-based intelligent search and analytics platform for Wall Street, the News database provides ResoluteAI's clients with a robust offering of timely scientific content. Foundation is a multi-source research hub that allows public scientific content to be searched as if it's single-source. ResoluteAI applies the most sophisticated artificial intelligence and machine learning to unstructured content. This AI-driven solution creates structured metadata and organizes it into datasets that include Companies, Patents, Grants, Clinical Trials, Technology Transfer, and Publications.
Far more people in the US may have died from opioids in the past two decades than previously reported, according to a new analysis of unclassified drug deaths carried out using machine-learning algorithms. Elaine Hill and her colleagues at the University of Rochester, New York, were examining data on drug overdose deaths when they realised that 22 per cent of such cases reported between 1999 and 2016 were listed on death certificates as overdoses without specifying the substance involved. "We found that remarkable, given the scale of the issue," says team member Andrew Boslett. The team tried to estimate what percentage of these unclassified deaths were due to opioids by analysing the coroners' and medical reports from opioid overdoses and unclassified overdoses. First, the researchers used machine-learning algorithms to analyse deaths that had been recorded as being due to opioid overdose.
Artificial intelligence is at the forefront of the minds of many pharmaceutical and health care executives. We know this because, as life sciences consultants, our clients frequently ask us for advice on how best to navigate AI. But along with enthusiasm in areas as diverse as phenotypic screening, drug repositioning, and analysis of CT scans, we are also finding a growing skepticism: What is real and what is hype? An example often cited by skeptical clients are the problems surrounding IBM Watson Health, especially in the cancer treatment sphere, where reporting by STAT and the Wall Street Journal, among others, has revealed a chasm between the public relations stories and the reality as experienced by clinicians. Now is an appropriate time to ask: What is holding back artificial intelligence in health care and the life sciences?