Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data, and deciding the best action(s) to take to achieve the given goal. Artificial intelligence for health includes machine learning (ML), natural language processing (NLP), speech recognition (text-to-speech and speech-to-text), image recognition and machine vision, expert systems (a computer system that emulates the decision-making ability of a human expert), robotics, and systems for planning, scheduling, and optimization. ML is a core component of AI that allows systems to automatically learn and improve without being explicitly programmed. Computer programs access data and use it with the aim of learning without human intervention or assistance and adjust actions accordingly.
Top1. Introduction
Deep learning (DL), a type of ML, is inspired by the human brain and uses multi-layered neural networks to find complex patterns and relationships in large datasets that traditional ML may miss. NLP is a subfield of AI that helps computers understand, interpret and manipulate human language. It is precisely AI’s ability to carry out speedy processing and analysis of datasets that is one of its key strengths. With more countries perfecting health informatics and electronic medical records (EMR), AI will become increasingly useful.
Unlike the first generation of AI systems, which relied on the curation of medical knowledge by experts and on the formulation of robust decision rules, recent AI research has leveraged machine learning methods, which can account for complex interactions, to identify patterns from the data. The recent renaissance in AI largely has been driven by the successful application of deep learning — which involves training an artificial neural network with many layers (that is, a ‘deep’ neural network) on huge datasets — to large sources of labelled data. AI has its footprint right from when a patient enters the doctor’s office.
Decision support systems that collect medically relevant information and provide suggestions to clinicians have been developed. At a more pedestrian level, increasingly AI will also be incorporated in healthcare-related software, such as the analysis of emergency calls to detect cardiac arrest. AI’s ability to use a universe of data to solve a narrow problem resonates with advocates of customized medical treatments. Knowing the health outcomes of millions of other people with similar symptoms, prognoses, and ages is invaluable to providers that promise one-of-a-kind pharmaceuticals, physiotherapies, and other actions designed to deliver the greatest benefit with the fewest side effects. A startup called Turbine uses AI to design personalized cancer-treatment regimens. The technology models cell biology on the molecular level and seeks to identify the best drug to use for specific tumors.
Recently, applications of medical-image diagnostic systems have expanded the frontiers of AI into the domains of human experts. This frontier continues to expand into other areas of medicine, such as clinical practice, translational medical and basic biomedical research.
Today’s diagnostic paradigm in medicine focuses on the detection of visually recognizable findings that suggest discrete pathologies in images. However, the focus of such detection of singular disease processes may miss concurrent conditions in an individual as a whole. Imaging methods, from simple x-rays to advanced, cross-sectional imaging methods such as MRI or CT, provide the opportunity to assess cancer in the context of its surrounding organ system.
With the integration of AI, complex assessments of biological networks may have a profound impact on the assessment of response and prognosis and treatment planning. In addition to the finding of a neoplasm, imaging may detect changes in the adjacent or distant organs beyond the tumor that alter patient susceptibility to systemic morbidities, which ultimately can contribute to mortality. This may occur as a byproduct of disease progression itself or as a byproduct of treatment, such as radiation or chemotherapy.
For example, in patients undergoing treatment for thoracic or breast cancer, chemotherapy may lead to myocardial damage, whereas radiation therapy promotes advanced coronary atherosclerosis; patients who survive cancer also experience a high rate of cardiovascular events. Collectively, these cardiotoxicities may confer a signal on routine imaging during the monitoring of cancer and be detected in earlier stages of development with comprehensive analytical systems that capture the diorama of disease processes. Initial concepts to apply AI to this clinical scenario originated from the finding that thoracic cancers and cardiovascular pathology are adjacent to each other and may be detected simultaneously (ie, coronary calcification or pericardial fat on chest CT).
The development of automated, AI-based detection and quantification algorithms therefore would enable the assessment of cardiometabolic markers without the need for additional imaging. In this manner, the role of AI can be extended to screening by simultaneously evaluating additional risks from the same data source. Because health care ultimately aims to prevent disease, the generation of accurate risk models is essential in guiding actionable risk-modification strategies.