It\u2019s no secret that the future of nearly every industry will involve innovative applications of data. In the healthcare industry, specifically, large-scale, data-driven decision making has the potential to generate as much as $100 billion of value by improving the efficiency of research and clinical trials and building new tools for physicians, consumers, insurers, and regulators that improve and personalize the patient experience.\nArtificial intelligence (AI) tools are poised to play a significant role in what may ultimately be a transformative era for the industry \u2014 specifically those featuring \u201cdeep learning\u201d capabilities. Research from growth consulting firm Frost & Sullivan suggests that the healthcare AI market will experience a compound annual growth rate of 40 percent through 2021, at which point it will account for over $6.5 billion of earned revenues.\nImpressive progress in AI-based diagnostics\nA variety of research confirms that this transformation is indeed already underway. In 2016, researchers from Beth Israel Deaconess Medical Center and Harvard Medical School used a deep learning algorithm to assess whether a cluster of lymph node cells contained cancer. Their algorithm achieved a diagnostic success rate of 92 percent, slightly below the 96 percent success rate achieved by human diagnosticians.\nIn a more experimental vein, Israeli researchers created an AI-based device designed to recognize \u2014 and differentiate among \u2014 17 different disease conditions, including chronic kidney failure, pulmonary arterial hypertension, and lung cancer based only on samples of patients\u2019 breath. Using an artificially intelligent nanoarray built with molecularly modified gold nanoparticles, the team assessed breath samples from 1,404 patients, correctly diagnosing 86 percent of them.\nWhere we really stand\nWith studies like these emerging more frequently, it can be challenging to discern what it all really means for the industry \u2014\u00a0both in the immediate future and far beyond.\nConsidering that diagnostic errors contribute to roughly 10 percent of patient deaths and between 6 percent and 17 percent of all hospital complications, AI-driven technologies in the mold of those outlined above have the potential to make substantial contributions to the healthcare industry and the patients it serves. Not only are algorithms quickly approaching human-level diagnostic success rates, they are doing so on timelines of which humans are quite literally incapable. In healthcare \u2014 where time is a precious resource \u2014 this efficiency is tremendously valuable.\nBut as promising as many of these explorations of AI-based diagnostics may be, it\u2019s critical to place them in their proper context. For one, AI is unlikely to replace your doctor anytime soon. While its diagnostic success rates are undoubtedly impressive \u2014 and will only get better with subsequent refinements \u2014 they don\u2019t always tell the whole story. In short, contrary to what the headlines may have you believe, when it comes to healthcare diagnostics, statistical accuracy is not the only metric about which we need to be concerned. In fact, this is one of the major reasons why, even as these technologies mature, human diagnosticians needn\u2019t fear being replaced by a robot.\nUnderstanding the diagnostic process\nOne of the primary reasons why physicians and diagnosticians are insulated from automation-based job loss is the nature of differential diagnosis itself, a stepwise process which has dominated medical practice for decades \u2014 and will continue to do so for the foreseeable future. After gathering facts about a patient\u2019s specific condition and general background and generating a list of potential etiologies, a diagnostician will commonly order a slate of tests in order to narrow the list of possible culprits.\nAccording to Saatchi & Saatchi Wellness\u2019 Group Medical Director Dr. Kavin Shah, \u201cThe screening process will often involve laboratory tests that are designed to detect particular markers of a specific disease. For example, the prostate-specific antigen (PSA) test for prostate cancer measures blood concentrations of PSA, a protein produced by the prostate gland.\u201d\nAs Dr. Shah continues, however, \u201cMany medical evaluations and tests may be thought of as part of the screening process, as well.\u201d From blood pressure tests and routine EKGs to mammograms and questionnaires about personal behavior and risk factors, there are a wide variety of non-lab tests that help diagnosticians hone in on a patient\u2019s specific ailment.\nUltimately, none of these is definitive in isolation. \u201cThey raise a heightened suspicion of disease,\u201d says Dr. Shah, \u201cbut they aren\u2019t diagnostic. A definitive diagnosis generally requires more extensive, more reliable, and frequently more invasive evaluations.\u201d In other words, outside of unusually simple cases, physicians rely on multiple tests \u2014 and multiple kinds of tests \u2014 to arrive at their final diagnosis.\nForging a human-machine partnership\nDiagnostic certainty dramatically increases once a test is repeated and, especially, once supplemental tests are run. In the end, this is where the true potential of AI-driven diagnostic tools lies. Getting a second opinion matters, and AI can serve as that second opinion.\nThe more tests we run, the more precise our diagnoses become, and deep learning algorithms like the one developed by the Beth Israel\/Harvard team can help medical professionals perform more tests more efficiently than ever before. It will remain a human medical professional\u2019s job to dictate which tests need to be run and when but forging a collaborative partnership with AI-based tools will empower us to amplify the productivity of the diagnostic processes we\u2019ve been executing on our own for decades. In short, when it comes to healthcare diagnostics, AI has the potential to bring about a change in scale, not in kind.\nAnd, according to deep learning pioneer Sebastian Thrun, this \u2014 not robot doctors \u2014 has always been the plan. \u201cI\u2019m interested in magnifying human ability,\u201d he told The New Yorker. \u201cJust as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.\u201d\nFor patients across the world, this can only be taken as good news.