Deep Dive: How a Health Tech Sprint Pioneered an AI Ecosystem

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When we began our 14-week tech health sprint in October 2018, we did not realize the profound lessons we would learn in just a few months. Together with federal agencies and private sector organizations, we demonstrated the power of applying artificial intelligence (AI) to open federal data. Through this collaborative process, we showed that federal data can be turned into products for real-world health applications with the potential to help millions of Americans have a better life. Joshua Di Frances, the executive director of the Presidential Innovation Fellows (PIF) program, says that this collaboration across agencies and private companies represents a new way of approaching AI and federal open data. "Through incentivizing links between government and industry via a bidirectional AI ecosystem, we can help promote usable, actionable data that benefits the American people," Di Frances said.


How Artificial Intelligence is Transforming Clinical Trial Recruitment

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The medical world is shifting underneath our feet. To keep up with the rising demands of empowered patients, physicians and pharma businesses regularly test innovative treatments and medicines during rigorous clinical trials. But one misguided move can trigger a domino effect, such as when the wrong patients are selected for a clinical trial. Today's infographic comes to us from Publicis Health, and it highlights why the current model of clinical trial recruitment urgently needs to change. Clinical trials help to determine if a new treatment, drug, or device is safe for the larger patient population.


The Role of Artificial Intelligence in Clinical Trial Transparency – Certara

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European and U.S. clinical trial data transparency initiatives -- such as EMA Policy 70 -- are creating additional disclosure compliance requirements for pharma and biotech companies. These transparency initiatives have, at their core, the distribution of clinical trial data for public consumption. Clinical trial data typically are contained within regulatory documents such as Clinical Study Reports (CSRs), Marketing Application Submission Documents (NDAs, MAAs, BLAs, etc.) and others. To achieve compliance with these mandates, pharma and biotech companies will need to anonymize and de-identify data sets in their clinical study reports and submission documents, produce research summaries suitable for a lay audience, and publish their clinical study information publicly. In this webinar, Synchrogenix President, Keith Kleeman will discuss how Artificial Intelligence (AI) and natural language recognition and processing are significantly improving the accuracy and efficiency of successfully anonymizing personally identifiable information, patient protected data, company confidential information and other sensitive information from clinical trial documents for public disclosure.


NIH redefines clinical trials, attracting critics

Science

A new National Institutes of Health (NIH) policy aimed at boosting the rigor and transparency of clinical trials is triggering concerns among many behavioral scientists. They are worried that the agency's widening definition of clinical trials could sweep up a broad array of basic science projects studying the human brain and behavior that do not test treatments. The clinical trials designation would impose a raft of new requirements on work that has already passed ethics review, such as different standards for applications submitted for funding, and a mandate to report results on clinicaltrials.gov, Critics say that would result in wasted resources and public confusion. NIH officials say they are still determining which behavioral studies will be defined as clinical trials.


Crowdsourcing a Comprehensive Clinical Trial Repository

AAAI Conferences

We present the open problem of building a comprehensive clinical trial repository to remove duplication of effort from the systematic review process. Arguing that no single organization has the resources to solve this problem, an approach based on crowdsourcing supplemented by automated data extraction appears to be the most promising. To determine the feasibility of this idea, we discuss the key challenges that need to be addressed.