White House holds summit on artificial intelligence with industry
President Trump’s Fiscal Year 2019 budget designates AI as an R&D priority for federal research.
The White House on Thursday hosted a summit on artificial intelligence, bringing together more than 100 business leaders, government officials and technical experts to discuss AI’s potential for making sense of the data that is inundating healthcare and other industries.
Representatives from agriculture, energy, financial services, healthcare, manufacturing and transportation attendaed the summit to discuss a number of topics, including research and development, workforce development, regulatory barriers to AI innovation and sector-specific applications of the technology. Tech giants such as Amazon, Facebook, Google and Microsoft also participated.
“Artificial intelligence holds tremendous potential as a tool to empower the American worker, drive growth in American industry and improve the lives of the American people,” said Michael Kratsios, deputy assistant to the President for technology policy. “Our free market approach to scientific discovery harnesses the combined strengths of government, industry and academia, and uniquely positions us to leverage this technology for the betterment of our great nation.”
According to the White House, President Trump’s Fiscal Year 2019 budget request was the “first in history” to designate AI as an administration R&D priority for federal research. “In the health realm, NIH is supporting the use of high-performance computing to analyze large data sets to drive cancer research forward,” states the administration’s budget request.
In addition, the Food and Drug Administration last month approved the first medical device to use AI to detect greater than a mild level of the eye disease diabetic retinopathy in adults who have diabetes—a condition that leads to vision loss for about 24,000 Americans annually.
Also See: FDA clears AI-based system to detect diabetic retinopathy
Frost & Sullivan predicts that AI has the potential to improve health outcomes by 30 to 40 percent, not by replacing the decision-making of healthcare professionals but by giving them new insights into the vast amount of health data that clinicians would be incapable of sifting through on their own.
While AI can create actionable insights for clinicians to make better choices about how to personalize treatment for patients, the breakthrough technology also brings with it challenges and risks. Trust that AI will not make a mistake is a serious challenge in healthcare where patient lives are literally on the line, particularly when it comes to leveraging the technology for clinical decision support.
Dario Gil, vice president of AI and quantum computing at IBM, testified in December 2017 before a Senate subcommittee that physicians using such systems to support medical decision making may be required to provide specific explanations for a diagnosis or course of treatment, both for regulatory and liability reasons.
“In these cases, the system will need to provide the reasoning and motivations behind the recommendation, in line with existing regulatory requirements specific to that industry,” Gil said.
According to Gurjeet Singh, executive chairman and co-founder of Ayasdi, a machine intelligence software company, this “black box” phenomenon “haunts proponents of AI in every industry, but it’s particularly problematic in healthcare, where the method used to reach a conclusion is vitally important.”
Representatives from agriculture, energy, financial services, healthcare, manufacturing and transportation attendaed the summit to discuss a number of topics, including research and development, workforce development, regulatory barriers to AI innovation and sector-specific applications of the technology. Tech giants such as Amazon, Facebook, Google and Microsoft also participated.
“Artificial intelligence holds tremendous potential as a tool to empower the American worker, drive growth in American industry and improve the lives of the American people,” said Michael Kratsios, deputy assistant to the President for technology policy. “Our free market approach to scientific discovery harnesses the combined strengths of government, industry and academia, and uniquely positions us to leverage this technology for the betterment of our great nation.”
According to the White House, President Trump’s Fiscal Year 2019 budget request was the “first in history” to designate AI as an administration R&D priority for federal research. “In the health realm, NIH is supporting the use of high-performance computing to analyze large data sets to drive cancer research forward,” states the administration’s budget request. In addition, the Food and Drug Administration last month approved the first medical device to use AI to detect greater than a mild level of the eye disease diabetic retinopathy in adults who have diabetes—a condition that leads to vision loss for about 24,000 Americans annually.
Also See: FDA clears AI-based system to detect diabetic retinopathy
Frost & Sullivan predicts that AI has the potential to improve health outcomes by 30 to 40 percent, not by replacing the decision-making of healthcare professionals but by giving them new insights into the vast amount of health data that clinicians would be incapable of sifting through on their own.
While AI can create actionable insights for clinicians to make better choices about how to personalize treatment for patients, the breakthrough technology also brings with it challenges and risks. Trust that AI will not make a mistake is a serious challenge in healthcare where patient lives are literally on the line, particularly when it comes to leveraging the technology for clinical decision support.
Dario Gil, vice president of AI and quantum computing at IBM, testified in December 2017 before a Senate subcommittee that physicians using such systems to support medical decision making may be required to provide specific explanations for a diagnosis or course of treatment, both for regulatory and liability reasons.
“In these cases, the system will need to provide the reasoning and motivations behind the recommendation, in line with existing regulatory requirements specific to that industry,” Gil said.
According to Gurjeet Singh, executive chairman and co-founder of Ayasdi, a machine intelligence software company, this “black box” phenomenon “haunts proponents of AI in every industry, but it’s particularly problematic in healthcare, where the method used to reach a conclusion is vitally important.”
More for you
Loading data for hdm_tax_topic #better-outcomes...