Artificial Intelligence (AI)-powered technologies are being adopted and are set to revolutionize the healthcare industry. It has been defined by Alan Turing as the science of making intelligent machines. AI also called expert systems can use algorithms to learn, evaluate features from a large amount of healthcare data, and then use the insights to assist clinical practice. It can also be augmented with learning and self-correcting abilities to improve its accuracy based on feedback. AI uses technologies like language processing and machine systems that can bring significant changes in the way we perform laboratory medicine. AI affects both automated lab and microscopy in general and is likely to further change the functioning of a modern lab. AI in automated lab and all its implications of Machine Learning are major technological innovations. AI is often described as augmented intelligence.
While AI uses sophisticated software to augment computers, machine learning uses cognitive algorithms to evaluate and learn from data consolidation, improve and further draw conclusions which help solve complex problems. By applying AI software to automation, quicker turnaround time can be achieved. AI has great role in determining the predictive maintenance, which is a great cost-saving measure. AI platform identifies pain points (namely, drivers of costs, waste, or inefficiency) does their analysis and finally the decision making. AI-based diagnostics reduces turnaround time than traditional microscopic testing and allows remote tele-consultation by digital pathology. Whole-slide imaging and computer vision takes picture of the entire area on the slide instead of the limited view as done by traditional microscopy. During image annotation, the pathologist plays a key role. This entire image can be sent to specialists across the globe for seeking their opinion. Algorithms are developed using an array of high-performance virtual machines, and developed algorithms are deployed on dedicated servers.
However, the role of the pathologist is central to both algorithm development and its execution. In one study on whole-slide images at Dartmouth Medical Center, a deep-learning model was used to classify histological patterns on surgical resection slides and final evaluation compared with three pathologists. The model was found at par with pathologists and does accurate classification of histological subtypes where it even had slight edge over the naked eye.
Recent studies in next-genomic sequencing have clearly shown that machine learning technologies analyze large sets of genomic data and help to identify novel gene functions. Artificial Neural Networks (ANN), are useful in various OMIC technologies.
As the world becomes more connected, laboratories will see that AI can address connectivity/communication issues related to clinicians and patients. Before AI systems can be deployed in healthcare applications, AI and machine systems need to be validated to avoid bias related to clinical data. Also Internet of Things, in future, will possibly be connected with sensor-based medical devices and can derive large amount of clinical data.
Recently Microsoft has partnered with SRL to expand AI network, particularly histopathology, and will build and develop machine learning models by leveraging SRL’s expertise and repository of histopathology slides as well as other data. AI is likely to reduce diagnostics errors by collaboration and integration of health information technologies (health IT). AI and machine learning projects track patient’s health and trends. This personalized service can be provided by diagnostic labs in upcoming years. Predictive analytics based on historical data trends and future predictions are part of smart analytics. Recent advances in artificial intelligence have led to speculation that AI might reduce work force in lab and is more likely to assist pathologists in the present scenario. In future, regulatory guidelines and legal framework will be needed regarding data management and monitoring of AI systems. Overall, AI is likely to have a positive impact on the functioning of diagnostic lab, resulting in time saving, transforming patient experience, increasing efficiency, and help making healthcare more accessible.