Augmented intelligence: A synergy between man and the machine

Abstract

The synergy between humans and artificial intelligence (AI) is emerging as an effective weapon to address the current blemishes of medicine. These blemishes include poor predictive power, susceptibility to fatal diagnostic and therapeutic errors, unintended consequences of empirical decision-making and inefficient hospital workflows, resulting all-too-often in suboptimal patient care. AI is on track to revolutionize how urologists will care for their patients. AI comprises the science, engineering, and development of systems that exhibit the characteristics which mimic human intelligence and behavior. Effective AI involves distinct insights in perception; in pattern recognition for text, speech, and images; in decision-making and for problem-solving. Rapid strides have been made in developing synergistic human-machine systems that exploit the positive aspects of human and AI-generated reasoning. It is, therefore, important for urologists to understand the current clinical applications of AI and its imminent impact on their practice in the coming years. At a high level, AI consists of four subfields that are leading to applications in health care: Machine learning (ML): statistical techniques-based programming, which enables computer systems to learn and recognize patterns to make predictions without being explicitly given the instructions for how to do so Natural Language Processing (NLP): the techniques to build computer power to achieve a human level of understanding of different languages. Language translation, text analysis, and speech recognition are some applications made possible through NLP Artificial Neural Networks (ANNs): it comprises computer programming to mimic biological nervous systems. Deep learning (DL) is the most recent iteration of this concept, comprising multiple layers of computer-generated neurons, which together have the ability to recognize more complex and subtle patterns than ever before Computer vision: through which machines learn to understand the data within radiologic and pathological images and endoscopic videos. With significant “training,” AI has already been shown to equal or exceed the current human level expertise to recognize tumors found within diagnostic images.[1] DL currently being applied to autonomous vehicle technology is another example of the enormous untapped potential AI has for medicine. With new programming, DL could be used for influencing patient care outcomes in positive ways. Autonomous driving is made possible thanks to an incredibly large amount of real-time traffic data, maps, and a myriad of live, real-time sensors aboard a vehicle monitoring surrounding road conditions. This constant stream of data enables the AI on-board the car to make instant crucial decisions unassisted by a driver. Each autonomous car also continually transmits information to a central cloud for statistical optimization as a feedback loop for its algorithms.[2] This technology currently in use evinces promise for future application of DL to make surgery safer through AI-driven intelligent robots capable of steering surgeons through complex surgical tasks smoothly making the use of volumes of data difficult to assimilate otherwise. As of now, such high-end DL applications in medicine are still in infancy. The Mako joint replacement robot is an intelligent robot which guides surgeons in planning and executing a personalized procedure for each patient with highly predictive postoperative outcomes. Patient records generate a staggering amount of data during the health-care process, through wearables and trackers; in digital and paper forms found within electronic medical records; in high-resolution radiologic, endoscopic, and histopathology images; and from the genomic information gathered during illness and recovery. This accumulated big data, when analyzed with AI, has an enormous potential to provide clinicians with an enlightened understanding of the patient's personalized disease pattern. AI