Abstract
AbstractA phenomenal surge in data generation, accessibility, storage, and processing hardware capacitates the artificial intelligence (AI)-based learning algorithms to solve the nature-inspired complex problems in day-to-day life. Fine-tuned intelligent models can outperform human involvement in a repertoire of domains, significantly impacting performance and productivity metrics. AI technologies are being utilized nowadays in several fields of application in healthcare, such as predictive modeling for neurological disorders, bioinformatics, surgical procedures, physical rehabilitation, medical robots, and management of clinical data in hospitals. With the ever-evolving nature of state-of-the-art learning models, the influence of AI in healthcare is constantly changing. This necessitates the need for a comprehensive review of the current scenario of AI applications in healthcare. In this work, a systematic review of AI technologies in the different levels of healthcare, ranging from benign research to full-fledged clinical setups, has been carried out for the last 10 years. Primarily, the integration of AI tools in three broad categories of healthcare, i.e., clinical settings, biomedical computations, and pharmaceutical industries, is introduced. Thereafter, this work provides an overview of fundamental and advanced forms of different AI techniques with recent developments. A PRISMA statement is provided to show the inclusion and exclusion criteria of the articles reviewed while conducting the systematic review. Based on articles selected and reviewed, the application of AI in different clinical settings is presented in a comprehensive yet systematic manner. Furthermore, the ethical and legal issues about AI implementation in healthcare domains are reviewed. The challenges and possible opportunities of deploying AI in real-life settings are discussed. Finally, the conclusion of the current work is presented. This review will engage the researchers to understand the merits and demerits of applying AI for healthcare applications.KeywordsArtificial intelligenceLearning algorithmsHealthcareClinical settingsEthical and legal AI
Published Version
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