The excitement over generative AI--and AI in general--has reached the multi-trillion-dollar healthcare industry, driven by the news of ChatGPT passing the United States Medical Licensing Exam (USMLE) and the rapid introduction of new healthcare-related AI applications. Bill Gates, for example, is recommending the use of generative AI tools for primary diagnoses of patients. While acknowledging that AI will inevitably misdiagnose patients, Gates argues that the upside is worth it. Preventing misdiagnoses that can impact, at the very least, a patient's quality of life, depends a lot on the quality and availability of the health data that is fed into the AI model. The current excitement notwithstanding, the development of AI healthcare solutions has been severely constrained by the dearth of comprehensive and representative real-world health data.

Blinken warns China that assisting Russia with Ukraine would be a'serious problem' Supreme Court hears defense of President Joe Biden's student loan forgiveness plan Ukraine forces claim to have'repelled' Russia's attacks on Bakhmut region It's almost hard to remember a time before people could turn to "Dr. Some of the information was wrong. Much of it was terrifying. But it helped empower patients who could, for the first time, research their own symptoms and learn more about their conditions. Now, ChatGPT and similar language processing tools promise to upend medical care again, providing patients with more data than a simple online search and explaining conditions and treatments in language nonexperts can understand. For clinicians, these chatbots might provide a brainstorming tool, guard against mistakes and relieve some of the burden of filling out paperwork, which could alleviate burnout and allow more facetime with patients. Get all the news you need in your inbox each morning. But – and it's a big "but" – the information these digital assistants provide might be more inaccurate and misleading than basic internet searches. "I see no potential for it in medicine," said Emily Bender, a linguistics professor at the University of Washington. By their very design, these large-language technologies are inappropriate sources of medical information, she said. Others argue that large language models could supplement, though not replace, primary care. "A human in the loop is still very much needed," said Katie Link, a machine learning engineer at Hugging Face, a company that develops collaborative machine learning tools. Link, who specializes in health care and biomedicine, thinks chatbots will be useful in medicine someday, but it isn't yet ready. And whether this technology should be available to patients, as well as doctors and researchers, and how much it should be regulated remain open questions. Regardless of the debate, there's little doubt such technologies are coming – and fast. ChatGPT launched its research preview on a Monday in December. By that Wednesday, it reportedly already had 1 million users. Earlier this month, both Microsoft and Google announced plans to include AI programs similar to ChatGPT in their search engines. "The idea that we would tell patients they shouldn't use these tools seems implausible.

For AI to flourish in healthcare, the industry must focus on the "algorithmically underserved," said John D. Halamka, M.D., M.S., president of Mayo Clinic Platform, at the HLTH 2022 conference this month in Las Vegas. Giving visibility to the algorithmically underserved -- individuals who do not generate enough data/are not well represented enough in health data sets for AI to make a determination -- is just one requirement to overcome the prospect of AI bias in healthcare. And identifying and fixing sources of AI bias must be a focus area for an industry that's striving for ethical and equitable AI development, shared Halamka. Dr. John Halamka is President of Mayo Clinic Platform, and a founding member of the Coalition for ... [ ] Health AI For example, what if there was a national registry that hosted all the metadata needed to power the responsible development of algorithms for use in healthcare? Building this kind of standardization into the relatively black box nature of AI development is among the priorities of The Coalition for Health AI (CHAI), which launched earlier this year.

The Coalition for AI Health has announced it will meet this month to finalize its consensus-driven framework and share recommendations by year-end in a progress update. CHAI convened in December to develop consensus and mutual understanding with goals to tame the drive to buy artificial intelligence and machine learning products in healthcare and arm health IT decision-makers with academic research and vetted guidelines to help them choose dependable technologies that provide value. Through October 14, CHAI is accepting public comments on its work examining testability, usability and safety at a workshop with subject-matter experts from healthcare and other industries the organization held in July. Previously, CHAI produced a sizable paper on bias, equity and fairness based on a two-day convening and accepted public comments until the end of last month. The result will be a framework, the "Guidelines for the Responsible Use of AI in Healthcare," that intentionally fosters resilient AI assurance, safety and security, according to the October 6 progress update. "Application of AI brings a tremendous benefit for patient care, but so is its potential to exacerbate inequity in healthcare," said Dr. John Halamka, president of Mayo Clinic Platform and cofounder of the coalition in the update.

Many have hailed the potential of artificial intelligence to transform healthcare. Michael Howell, Google's chief clinical officer and deputy chief health officer, says, "It's hard to imagine a technology that is more hyped than AI." Even so, Stephen Parodi, executive vice president of The Permanente Federation, says, "Widespread AI use in healthcare is still in its infancy." Still, many are projecting significant growth in the prevalence of AI in medicine in the near future. During a one-hour forum hosted by The Permanente Federation Monday, healthcare leaders, all physicians, assessed the possibilities of AI, the keys to success, and expectations on its future uses.

Data used well via artificial intelligence and transparent algorithms offers health care a glimpse into democratization and equitable, efficient and efficacious care, according to an expert speaking at The Liver Meeting Digital Experience. "Over the next six quarters, ... we are going to see technology advancements, we are going to see policy and regulatory change and cultural expectations that will ask us to deliver cures in novel settings using novel methods and processes that will require us as providers to rethink how health care works in this country and internationally," John D. Halamka, MD, MS, president of the Mayo Clinic Platform, said during his President's Choice Lecture. Halamka challenged meeting attendees to adjust their views of what a platform is and what it offers to both physicians and patients. "Whether you're a provider or just a care navigator for a family ... in 2020 and 2021, care is often challenging to coordinate. It's not clear where you go next, what disease state you have, ... bringing the right patient to the right facility ... to get the right care ... is guesswork," he said.

Artificial intelligence's applicability in healthcare settings may not have lived up to corporate ... [ ] and investor hype yet, but AI experts believe we're still in the very early stages The "AI in healthcare: myth versus reality" discussion has been happening for well over a decade. From AI bias and data quality issues to considerable market failures (e.g., the notorious missteps and downfall of IBM's Watson Health unit), the progress and efficacy of AI in healthcare continues to face extreme scrutiny. John Halamka, M.D., M.S., is President of The Mayo Clinic Platform As President of the Mayo Clinic Platform, John Halamka, M.D., M.S., is "not disappointed in the least" about AI's progress in healthcare. "I think of it as a maturation process," he said. But can your three-year-old add a column of numbers?

Any digital health conference features its share of machine learning evangelism. Technology executives give fervent testimonials about its power to save lives and money, to predict episodes of severe illness, to help hospitals root out inefficiency. This year's gathering of the Health Information Management Systems Society (HIMSS) in Las Vegas was no different. But in between the glowing anecdotes, an aggressive counter narrative emerged: Machine learning needs a watchdog. Throughout the four-day conference, the largest annual event in health care technology, industry leaders called for better ways to evaluate the usefulness of machine learning algorithms, audit them for bias, and put in place regulations designed to ensure reliability, fairness, and transparency.

Artificial intelligence – especially machine learning – is helping to drive progress and innovation throughout the health technology industry. But at the same time, it's clear that AI can also recreate and exacerbate biases, potentially worsening health disparities and even putting patients' lives at risk. In a HIMSS21 Global Conference Digital Session scheduled to air on Monday, August 9, Mayo Clinic Platform President Dr. John Halamka will speak with HIMSS Executive Vice President of Media Georgia Galanoudis about evaluating algorithms' fitness for purpose – and whether it's possible to maintain the promise of AI, knowing about its potential downsides. Although AI-related optimism is justified, Halamka says, issues around equity and bias replication – and algorithm quality in general – are going to be present whenever there are mass quantities of training data. But the industry doesn't generally publish statistics describing how an algorithm was developed, or how it can best be used.

One of the effects of the COVID-19 public health emergency is that it has added urgency and speed to technology transformations that were already occurring, such as cloud migration and deployments of artificial intelligence and machine learning. At few places is that shift more pronounced than at Rochester, Minnesota-based Mayo Clinic, which six months before the pandemic arrived in the United States had embarked on a decade-long strategic partnership with Google Cloud. "Our partnership will propel a multitude of AI projects currently spearheaded by our scientists and physicians, and will provide technology tools to unlock the value of data and deliver answers at a scale much greater than today," said Mayo CIO Cris Ross at the time. Shortly after the partnership was announced, toward the end of 2019, the health system hired longtime CIO Dr. John Halamka as president of Mayo Clinic Platform, tasking him with leading a cloud-hosted, AI-powered digital transformation across the enterprise. In the months since, like the rest of the world, Mayo Clinic has found itself tested and challenged by the pandemic and its ripple effect – but has also embraced the moment as an inflection point, a powerful moment to push forward with an array of new use cases to drive quality improvement, streamline efficiency, and boost the health of patients and populations in the years ahead.