When it comes to increasing the accuracy of medical diagnoses, reducing worker burnout, and providing cheaper universal healthcare, AI seems like a natural solution. AI appears to have secured a prominent role in the medical industry as both entrepreneurs and policymakers extol the immense potential in incorporating machine learning and deep neural networks into a doctor's daily routine. Decades worth of medical data collected from every appointment, procedure, and survey sit untouched in databases while algorithms wait hungrily for training data. Prominent applications of AI in predictive diagnostics lie in image-based diagnostics and preemptive predictions through machine learning. Amidst the bustling excitement over the applications of AI in healthcare, the medical industry maintains its slow and sluggish pace in adopting new technologies.
Machine Learning in the medical field will improve a patient's health with minimum costs. Use cases of ML are making a near-perfect diagnosis, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles. Diagnostic errors account for about 10% of yearly patient deaths, mostly due to issues like poor tracking, misinformation, and miscommunication.
When it comes to effectiveness of machine learning, more data almost always yields better results--and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments.
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.
Artificial intelligence (AI) is the enabling of machines to "think" like humans. The ability of AI to make data-driven decisions (pattern recognition) and to identify shared characteristics among data points are especially relevant to medicine (data mining). Spurred on by the explosive expansion of the Internet of Things (IoT) and the decreasing cost of cloud storage and computing, the AI health-care market is likely to exceed $34 billion by 2025, according to a report by Tractica. In this article, we will explore the ever-expanding application of AI in medical diagnosis, drug and device development, and operational improvement. IBM's Watson was the first AI platform to enter the field of medical research.