During the middle of the 20th century, scientists and social theorists began to fear the problem of overpopulation, predicting a period of mass starvation. Famously, Stanford's Paul Ehrlich, in his 1968 book, "The Population Bomb" predicted "the battle to feed all of humanity is over...hundreds of millions of people will starve to death in spite of any crash programs embarked upon now." At the time, his pessimistic thinking was not isolated. Simultaneously, Norman Borlaug became a pioneer in wheat production with his work in genetics powering new ways to grow crops. His "Green Revolution" for which he received the 1970 Nobel Peace Prize, is credited with saving over a billion lives.

Schwan Park, father of speed cuber Max Park, 21, tells Fox News Digital the story of his son's record-breaking achievement with Rubik's Cube: "We always knew he was good," he said. A mom of a child with autism is assuring other parents that their autistic children "do not need to be fixed" -- rather, they need to be better understood. Kelley Coleman, author of the upcoming book, "Everything No One Tells You About Parenting a Disabled Child," is encouraging other parents not to be afraid of seeking out diagnoses. "All that will do is keep us from being able to enable our children to be the best version of themselves," the Los Angeles-based mother of two said in an interview with Fox News Digital. Coleman's comments come as documented cases of autism spectrum disorder (ASD) have been on the rise.

Lifesaving Radio uses artificial intelligence to generate music at the ideal tempo for optimal surgical performance. Fox News Digital spoke to the team behind it. Artificial intelligence (AI) is indeed here and has been rapidly advancing in recent years. As such, artificial intelligence has also made its way into the doctor's office and has the potential to revolutionize the health care system in a number of ways. Machine learning can analyze algorithms and large data sets, identify patterns, and make predictions, assisting doctors in making more accurate diagnoses and treatments.

At the latest Fortune Brainstorm Health virtual discussion on Wednesday, experts from various parts of the medical field said that once these impediments are overcome, A.I. could be the key to improving patient outcomes, lowering overall costs, and reducing burnout and stress on overworked caregivers. One of the first steps, they agreed, is breaking down the barriers that prevent the collection and sharing of accurate, unbiased data. "It's perhaps the most important question of the day: how do we get systems to talk with each other?" said Dr. David Gruen, the chief medical officer of imaging at Merative. "[A.I.] has a broad concept of interoperability. How do we trust the data? How do we get unbiased data? How do we pull together the data that we have in our arms or in the apps on our phones into our health system's record so that we really get a comprehensive picture? We believe that that's going to be a huge hurdle [overcome] when we convince people that this is cost-saving, data-enhancing, and outcome-improving."

The last 10 years have brought tremendous growth in artificial intelligence. Consumer internet companies have gathered vast amounts of data, which has been used to train powerful machine learning programs. Machine learning algorithms are widely available for many commercial applications, and some are open source. Now it's time to focus on the data that fuels these systems, according to AI pioneer Andrew Ng, SM '98, the founder of the Google Brain research lab, co-founder of Coursera, and former chief scientist at Baidu. Ng advocates for "data-centric AI," which he describes as "the discipline of systematically engineering the data needed to build a successful AI system."

Artificial intelligence and medical algorithms are deeply intertwined with our modern health care system. These technologies mimic the thought processes of doctors to make medical decisions and are designed to help providers determine who needs care. But one big problem with artificial intelligence is that it very often replicate the biases and blind spots of the humans who create them. Researchers and physicians have warned that algorithms used to determine who gets kidney transplants, heart surgeries and breast cancer diagnoses display racial bias. Those problems can lead to detrimental care that, in some cases, can jeopardize the health of millions of patients.

This is the first episode in our series Smarter health. American health care is complex. In the first episode in our series Smarter health, we explore the potential of AI in health care -- from predicting patient risk, to diagnostics, to just helping physicians make better decisions. Today, On Point: We consider whether AI's potential can be realized in our financially-motivated health care system. Welcome to an On Point special series: Smarter health: Artificial intelligence and the future of American health care. In the not so distant future, artificial intelligence and machine learning technologies could transform the health care you receive, whether you're aware of it or not. Here are just a couple of examples. Dr. Vindell Washington is chief clinical officer at Verily Life Sciences, which is owned by Google's parent company, Alphabet. Washington oversees the development of Onduo. Technology that weaves together multiple streams of complex, daily medical data in order to guide and personalize health care decisions across entire patient populations. VINDELL WASHINGTON [Tape]: You might have a blood pressure cuff reading, you may have a blood sugar reading, you may have some logging that you've done.

AI represents a set of technologies that consist of automated systems able to perform tasks including visual perceptions, augmenting diagnostics and predictions, and seamlessly processing large quantities of data. Tasks performed by AI have promising applications in value-based care, including strengthening patient care and enhancing health outcomes. Data overload is an escalating problem afflicting health care systems across the continuum. Interpretable AI processes can simplify colossal amounts of complex data and synthesize key facets of the data for analysis by the proper specialist with recommendations and insights. This ability to digest and streamline data maximizes the valuable time a doctor can spend with patients.

Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations. Once virtual, the AI Cures Conference returned to in-person attendance at MIT's Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the "transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise."

The Covid-19 pandemic was the perfect moment for AI to, literally, save the world. There was an unprecedented convergence of the need for fast, evidence-based decisions and large-scale problem solving with datasets spilling out of every country in the world. For health care systems facing a brand new, rapidly spreading disease, AI was -- in theory -- the ideal tool. AI could be deployed to make predictions, enhance efficiencies, and free up staff through automation; it could help rapidly process vast amounts of information and make lifesaving decisions. Or, that was the idea at least.