AI News, Adopting AI in Health Care Will Be Slow and Difficult artificial intelligence

Adopting AI in Health Care Will Be Slow and Difficult

Artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces.

The goal of the Pre-Cert pilot is to help the FDA determine the key metrics and performance indicators required for product precertification, while also identifying ways to make the approval process easier for developers and help advance healthcare innovation.

Most recently, the FDA released in September its “Policy for Device Software Functions and Mobile Medical Applications” — a series of guidance documents that describe how the agency plans to regulate software that aids in clinical decision support (CDS), including software that utilizes machine-learning-based algorithms.

An example of CDS software that would fall under the FDA’s “higher-risk” oversight category would be one that identifies a patient at risk for a potentially serious medical condition — such as a postoperative cardiovascular event — but does not explain why the software made that identification.

To account for the shifting FDA oversight and approval processes, software developers must carefully think through how to best design and roll out their product so it’s well positioned for FDA approval, especially if the software falls under the agency’s “higher risk” category.

Robots and AI changing the healthcare industry

4차산업혁명, 의료의 새 흐름 Big data, Internet of Things, and Artificial Inteligence. The reach of the fourth industrial revolution can be felt in almost every industry.

Regulating Artificial Intelligence: How to Control the Unexplainable

The technologies we broadly call "AI" are changing industries, from finance to advertising, medicine and logistics. But the biggest hurdle to the adoption of ...

Getting to the Wrong Answer Faster with Your Analytics: Shifting to a Better Use of AI in Healthcare

Wrong conclusions in your analytics can cause waste and disillusionment, not to mention suboptimal outcomes that may take months or even years to recover ...

#222 Building an Artificial Intelligence (AI) Platform

Artificial Intelligence is surrounded by marketing hype, making it difficult to assess what's real and useful. In this episode, we talk with a venture capital investor ...

Susan Schneider: "Artificial You: AI and the Future of Your Mind" | Talks at Google

Susan Schneider, NASA's Baruch Blumberg Chair of Astrobiology and director of the AI, Mind, and Society groups at the University of Connecticut, explores ...

AI and Machine Learning in Health Programs

After viewing the webinar recording, please fill out our webinar evaluation form: ...

The Hugh Thompson Show: Artificial Intelligence APJ Style

Hugh Thompson, RSA Conference Program Chair, RSA Conference Panelists: Dr Ayesha Khanna, Co-Founder and Chief Executive Officer, ADDO AI Mahmood ...

Raising the Digital Trajectory of Healthcare

Table of Contents Q&A 1:14:29 Should healthcare be more digitized? Absolutely. But if we go about it the wrong way... or the naïve way... we will take two steps ...

Who is winning the artificial intelligence race?

Read the full story on the Stanford Engineering website: In a recent talk at Stanford, Kai-Fu Lee says China has taken the lead. Lee is ..

Allen School Distinguished Lecture: Jeff Dean (Google AI)

Lecture Title: Deep Learning to Solve Challenging Problems For the past eight years, Google Research teams have conducted research on difficult problems in ...