Photo credit: mHealth Intelligence
Last week marked a major step towards making digital health information available at scale. The much awaited information blocking rules, issued by the US Office of the National Coordinator for Health IT (ONC), came into effect, enforcing measures that prevent networks from choking off access to information while supporting the secure exchange of healthcare data.
The National Coordinator for IT, Micky Tripathi, has called the information blocking rules as well as the interoperability rules mandated by the 21st Century Cures Act “a paradigm shift for the industry.” This could be the inflection point for enabling access to highly valuable healthcare data for patients while also offering entrepreneurs the ability to build patient-centric solutions on the foundation of healthcare data.
These regulations should help enable the second wave of healthcare innovation with AI as a primary driver. With an acceleration of digitization and available datasets, we anticipate an explosion of solutions that leverage machine learning, computer vision and natural language processing to solve some of our most pressing healthcare challenges.
5 Noteworthy AI and Deep Tech Articles: week of Apr 12, 2021
1) Why computers won’t make themselves smarter (The New Yorker)
AI has proven itself in tackling specific problems and can reliably surpass human abilities when it comes to tasks such as identifying cancer in a radiology image or optimizing the efficiency of a data centre. But the technology still faces challenges when it comes to common sense and creativity. “For the foreseeable future, the ongoing technological explosion will be driven by humans using previously invented tools to invent new ones.” This means that human intelligence is unlikely to be replaced by AI, but also superhumanly intelligent AI is unlikely to advance humanity without intelligent human decision-making behind the machines.
2) AI uses patient data to optimize selection of eligibility criteria for clinical trials (Nature)
Most trials use eligibility criteria that restrict participants to those with low-risk profiles. Yet this approach prevents the inclusion of some people who could potentially benefit from the trial treatment. An accessible software tool to enable the systematic evaluation of eligibility criteria by emulating clinical trials using EHR data has been lacking. Researchers have addressed this deficiency by creating an open-source artificial-intelligence (AI) tool they call Trial Pathfinder which uses EHR data to compare the survival outcomes of individuals who did or did not receive a particular approved drug treatment.
3) FDA authorizes marketing of first device that uses artificial intelligence to help detect potential signs of colon cancer (FDA News)
AI and machine learning technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. The Food and Drug Administration (FDA) authorized marketing of the first device that uses machine learning to assist clinicians in detecting lesions (such as polyps or suspected tumours) in the colon in real time during a colonoscopy. While it’s the first for its application, the FDA has approved over 60 other AI/ML based medical devices and algorithms.
4) Human actors bring an AI-written script to life (Vice)
Software has been used to automatically generate scripts for Seinfeld, a Batman film, and a Hallmark Channel original, usually for hilarious results. But artists also use AI to generate novel ideas. Calamity AI is one example, another is AI researcher Ross Goodwin’s Sunspring, a 2016 experimental science fiction short film that was entirely written using neural networks. Many of these early art projects highlight AI’s progress toward identifying similarities and generalizing – a key skill needed to write a script that an audience can follow.
5) BMO found AI, climate change and diversity opportunities in pandemic: CEO (Canadian Press)
BMO Financial Group accelerated its investments in AI over the course of the pandemic, using the technology to model lending scenarios and help customers make quicker and better decisions about their finances. The BMO AI Capabilities Team also partnered with the Vector Institute to undertake a natural language processing (NLP) project. Over 182 million finance and market-related terms and their contexts were added to an enriched dataset which BMO used to fine-tune models that analyze market sentiments.
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