Artificial intelligence in healthcare: past, present and future

Abstract

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Current state and future prospects of artificial intelligence in ophthalmology: a review

“…To date, advances have been made predominantly in weak AI, so AI will be unlikely to replace most medical specialists for the foreseeable future. 8,12,13 Continuing on from this, we expect that significant headway will be made under medical imaging, given deep learning's particular proclivity for image processing. 8 In addition, combining the strengths of human clinicians with the strengths of deep learning systems should reduce errors in diagnostics and therapeutics that are inherent in our current system.…”

“…8 In addition, combining the strengths of human clinicians with the strengths of deep learning systems should reduce errors in diagnostics and therapeutics that are inherent in our current system. To date, advances have been made predominantly in weak AI, so AI will be unlikely to replace most medical specialists for the foreseeable future.…”

Artificial intelligence in medical devices and clinical decision support systems

“…Today, large numbers of smartphone apps, home appliances, and personal products like automobiles include ES and ML capabilities, enabling tasks like voice recognition, complex Global Positioning System (GPS)-based routing recommendations, and a growing number of Food and Drug Administration (FDA)-approved medical devices (Jiang, 2017).…”

“…The two large branches of AI, ES, and ML, could become much more accessible and "natural" for users if the keyboard and mouse can be eliminated with NLP (Jiang, 2017). This may be especially helpful for aging citizens with deteriorating vision, hearing, or hand-eye coordination limitations.…”

“…Several healthcare-related examples have been cited above, and applications in health care are multiplying rapidly thanks to apps and ubiquitous memory and cloud computing resources. Broad classes of Healthcare AI (HAI) include (Jiang, 2017;Shah, 2018) • vision and pattern detection (tumors, breaks, foreign objects, gait irregularities);…”