You visit your doctor for a routine checkup. She decides to do a fine needle aspiration for a seemingly insignificant lesion (\u201cIt\u2019s probably nothing\u2026\u201d). But a few days later you get a call back. They\u2019ve found some abnormal cells.\nYour doctor recommends a specialist who performs a resection in his office, sending a tissue sample to a histology lab. The lab\u2019s histologist preps the sample by taking sections, placing them in a \u201ccassette,\u201d then sending it overnight to another lab for processing. When the tissue blocks are returned the next day, a histologist cuts slides from the processed paraffin-embedded tissue and stains them with hematoxylin and eosin which differentially bind to cellular structures. A case number is created, whereupon the slides are placed on a plastic tray and delivered to the pathologist, as if serving hors d\u2019oeuvres. The pathologist might walk the tray down the hall to another pathologist, who examines the specimens under a microscope, sometimes recommending additional interpretive stains. A diagnosis is made, and the pathology report is sent back to your doctor, who then creates a treatment plan. The entire process can take weeks, sometimes even months.\nPatients and medical professionals have become accustomed to the drill: it\u2019s been standard practice for decades. But advancements in artificial intelligence are about to disrupt disease diagnosis.\nDiagnosis, accelerated\nImagine that a histologist can scan the slides, creating electronic images that can be accessed digitally by different physicians, labs, and technicians. Tissue samples can be stored in the cloud and shared with far-flung specialists who can collaborate from afar with care providers. Diagnoses can be corroborated through deep learning algorithms that recognize specific characteristics and behaviors in the tissue sample. The digital images become a historical record of the blood or tissue sample. The entire diagnostic process could go from weeks to hours, accelerating vital patient care.\n\u201cWe\u2019ve always had the discussion about whether pathology is art or science,\u201d explains Dave Billiter, CEO and co-founder of Deep Lens, a digital pathology AI and cloud platform vendor. Billiter comes from more traditional healthcare, holding executive positions at Cardinal Health and The Research Institute at Nationwide Children\u2019s Hospital. But he recognizes the promise of AI breaking new ground in medicine.\n\u201cIn essence you\u2019re relying on a trained eye to render a diagnosis of a biospecimen. But there are so many different types and subtypes of a disease. Even with highly trained medical professionals, you\u2019re relying on an individual to understand them all. It\u2019s an arduous process for a pathologist. Then you start adding all these different biomarkers, genomics, metabolomics\u2026 It\u2019s a pathology information tsunami!\u201d\nAs the need for more specialization increases, the number of pathologists is shrinking. According to the Medical Laboratory Observer, fewer medical school graduates are choosing pathology as a specialty, despite the increasing demand driven by an aging baby-boomer population and evolving genomic research.\n\u201cWe see a decreasing pool of pathology experts,\u201d confirms Saskia Boisot MD, a board-certified hematopathologist based in Orange County, California. \u201cPart of it is that medical schools don\u2019t feature careers in pathology as prominently as they do other specialties. Part of it is that the job market for pathologists is quite limited, the practice entirely dependent on affiliations with either hospitals or private reference laboratories, with little opportunity for autonomy.\u201d\nHumans, more than ever\nRecognizing the opportunity, medical schools are beefing up their emerging technology curricula. Med students are now offered courses in technology infrastructure, deep learning, and data management alongside their biology classes, adding GPUs, robotics, and convolutional neural networks to their training.\nCompanies like Deep Lens and Philips package images in secure environments and can automatically place them into a pathologist workflow. AI algorithms can optimize routing to specialists and others in a dynamic, collaborative environment.\nThe benefits don\u2019t stop at diagnosis. \u201cDigitized results could move the needle for clinical trials,\u201d says software investor and Deep Lens President and co-founder Simon Arkell. \u201cPatient enrollment is difficult and time consuming. AI can identify a cancer, feed new models, and help researchers quickly qualify clinical trial participants. Drug companies and CROs [Contract Resource Organizations] could experience huge economies of scale.\u201d\nThen there\u2019s the coming data deluge. According to Andrew Hessell, a biotech industry pioneer and now CEO of Humane Genomics, \u201cIt\u2019s economically realistic for a billion human genomes to come online over the next decade or two.\u201d It will be nearly impossible for pathologists to stay current with the emergence of new biomarkers without the help of machine learning and digitization.\n\u201cI think there\u2019s a fear that, much in the same way a lot of radiology interpretation is being outsourced through digitization, pathologists could become progressively obsolete,\u201d says Dr. Boisot. \u201cConventional anatomic pathology is undergoing a massive transformation, as many diagnoses are now being made based on molecular studies. While these studies currently only represent an adjunct to morphologic evaluation, there\u2019s a very real chance that they could dwarf the need for a pathologist\u2019s keen eye.\u201d\nToday\u2019s problems have fomented over decades. But the pathology discipline isn\u2019t going the way of the dinosaur. Billiter maintains that the pathology discipline will be central to personalizing medicine, with pathologists adopting new technologies drive faster, more accurate diagnostics. \u201cThis will be as much about humans helping AI as AI helping humans.\u201d\u00a0\nWhat better partnership could there be?