The manuscript titled "AlphaGo, deep learning, and the future of the human microscopist" in this month's issue of the Archives of Pathology & Laboratory Medicine1 describes the triumph of Google's (Mountain View, California) artificial intelligence (AI) program, AlphaGo, which beat the 18-time world champion of Go, an ancient Chinese board game far more complex than chess. The authors have hypothesized that the development of intuition and creativity combined with the raw computing of AI heralds an age where well-designed and well-executed AI algorithms can solve complex medical problems, including the interpretation of diagnostic images, thereby replacing the microscopist. Of note, in a prior work, the microscope was predicted to have a 75% chance of remaining in use for another 144 years.2 To support their hypothesis, the authors presented recent studies that compared the performance of nontraditional interpreters to those of experienced pathologists, in making accurate diagnoses (note: 1 author disclosed a significant financial interest in an AI company). One study examined the potential of using pigeons (yes, pigeons) for medical image studies,3 wherein the pigeons engaged in a matching game of completely benign and unambiguously malignant breast histology images.
"Almost all fields of artificial intelligence have applications in healthcare."1 Medicine appears to have entered the era of data, and artificial intelligence (AI) will prove a valuable tool in the future, notably as an aid to diagnosis. Watson, the program developed by IBM, is the most emblematic example. Based on deep learning, the best known branch of artificial intelligence, it operates by layers, like a network of interconnected neurons spread between different strata for each calculation. The answer is only "produced" after a learning process which from the start associates symptoms and pathology.
The Precision Medicine World Conference will be one of the most exciting conferences focused on AI in healthcare in 2018. CEOs of cutting edge companies from around the world will come together to discuss how they are using techniques such as computer vision, deep learning and machine learning to make big advances in medicine from drug discovery to patient diagnosis and treatment. The program will traverse innovative technologies and clinical case studies that enable the translation of precision medicine into direct improvements in health care. Attendees will have an opportunity to learn about the latest developments in Precision Medicine and cutting-edge new strategies that are changing how patients are treated.
Pathology artificial intelligence (AI) software developer Paige is highlighting a paper published July 15 in Nature Medicine that indicates the company's technology can be used to develop AI algorithms with "near-perfect accuracy" for analyzing pathology slides for prostate cancer, skin cancer, and breast cancer. In the paper, Chief Scientific Officer Thomas Fuchs, PhD, of Memorial Sloan Kettering Cancer Center and colleagues describe how a series of deep-learning algorithms for clinical decision support in pathology were developed with an automated training and testing technique. Fuchs is the senior author on the paper, with his student Gabriele Campanella as the first author. The deployment of clinical decision support for pathology has been hindered by the need to curate large, manually annotated datasets to test and train AI algorithms, the authors noted. Instead, Campanella et al present a system in which algorithms are trained using only the reported diagnoses.
Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3