When medical historians write about the coronavirus pandemic, they'll likely focus on the slow U.S. response and failures of leadership that led to a tragically high death toll. But that will be only part of the story. From the wreckage and devastation will emerge something few contemporary observers would expect: a brighter future for American healthcare. Five technologies, all previously underappreciated and underutilized, will help our nation move past the coronavirus crisis into a new, golden era of medicine. Like the seedlings of the eucalyptus tree, which sprout only after a forest fire, these technological solutions will blossom in the aftermath of the Covid-19 pandemic--turning U.S. healthcare's outdated and broken system into one that is more convenient, effective and affordable.

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In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable--hidden inside a so-called black box--how can it be trusted? Among researchers, there's a growing call to clarify how deep learning tools make decisions--and a debate over what such interpretability might demand and when it's truly needed.

Disclaimer This press release contains forward-looking statements within the meaning of the United States Private Securities Litigation Reform Act of 1995 that can generally be identified by words such as "to transform," "multiyear," "commitment," "to found," "aims," "vision," "potential," "can," "will," "plan," "expect," "anticipate," "committed," or similar terms, or regarding the development or adoption of potentially transformational technologies and business models and the collaboration with Microsoft; or by express or implied discussions regarding potential marketing approvals, new indications or labeling for the healthcare products described in this press release, or regarding potential future revenues from collaboration with Microsoft or such products. You should not place undue reliance on these statements. Such forward-looking statements are based on our current beliefs and expectations regarding future events, and are subject to significant known and unknown risks and uncertainties. Should one or more of these risks or uncertainties materialize, or should underlying assumptions prove incorrect, actual results may vary materially from those set forth in the forward-looking statements. There can be no guarantee that the collaboration with Microsoft will achieve any or all of its intended goals or objectives, or in any particular time frame.

Last month, President Trump signed an executive order making the development and regulation of artificial intelligence a federal priority. But one area where artificial intelligence is already taking hold is health care. Doctors are already using A.I. to spot potentially lethal lesions on mammograms. Scientists are also developing A.I. systems that can diagnose common childhood conditions, predict whether a person will develop Alzheimer's disease and monitor people with conditions like multiple sclerosis and Parkinson's disease. Dr. Eric Topol, a cardiologist and the founder and director of the Scripps Research Translational Institute, has long heralded this convergence of technology and medicine. Now, in a new book called "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again," Dr. Topol explores how A.I. is likely to transform almost everything that doctors do.

Dr. Topol believes that A.I. can do more than enhance diagnoses and treatments. It can also save doctors from doing tasks like taking notes and reading scans, allowing them to spend more time connecting with their patients. Recently, we caught up with Dr. Topol to discuss his thoughts on where A.I. has the most potential to improve health care, where it might stumble, and how it could protect doctors from things like burnout and depression. Here are edited excerpts from our interview. Q. Can A.I. help to lower America's soaring health care costs?

Q. Can A.I. help to lower America's soaring health care costs? The No. 1 line item of health care cost in America is human resources, which has recently grown -- as of December 2017 towering over retail -- to be the leading job source for our economy. By augmenting human performance, A.I. has the potential to markedly improve productivity, efficiency, work flow, accuracy and speed, both for doctors and for patients. Q. Can you talk about the area of medicine where A.I. shows the most promise? A. There are a few key areas. One is machine pattern recognition to promote the rapid and accurate reading of medical scans, slides, skin lesions, the pickup of small polyps during colonoscopy, and much more.

Machine learning is an often-used term that has been promised to do everything from making workers more productive to taking over individuals' jobs entirely. Frankly, it will likely be many years before anyone should be concerned about being replaced by artificial intelligence (AI) at their job. However, doctors might find AI impinging upon their jobs sooner rather than later. The medical field has some characteristics that make it an attractive target for machine learning. The high stakes nature of correct disease diagnosis, coupled with over-worked and fatigued doctors, can lead to cases where patients with easily treatable diseases go undiagnosed and suffer greatly from this. Combined with a bottleneck in diagnoses due to the limited number of doctors available and expensive diagnosis costs, machine learning algorithms can appear very attractive for both patients and clinics to implement.

The doctor enters and pulls up the electronic medical record. The patient's history is already there. The doctor drags and drops the image, presses the "analyze" button. An actionable diagnosis appears a moment later. If artificial intelligence (AI) were to one day take over much of clinical practice, as some fear or anticipate -- being potentially faster, more reliable, and generally better at certain tasks than humans -- clinical decisions may no longer depend on tired eyes, imperfect risk scores, or lagging guidelines.