Meta-analysis of applications of artificial intelligence health models in the pharmaceutical industry and healthcare system
Code: G-1215
Authors: Alireza Mirshahi * ℗, علی وحیدی سبزوار
Schedule: Not Scheduled!
Tag: Clinical Decision Support System
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Abstract:
Abstract
Background and aims: Artificial intelligence (AI) are a branch of computer science that allows machines to perform tasks more efficiently, and can process and analyze large amounts of complex data in a short amount of time. Today, research focused on AI has grown exponentially, and its role in healthcare and research is rapidly emerging. This review describes the opportunities and challenges of AI in healthcare research and the pharmaceutical industry. Method: Articles were collected from the Direct Science, PubMed, and Google Scholar databases using specific keywords and phrases such as “artificial intelligence,” “pharmaceutical industry,” “neural networks,” “deep learning,” “diagnosis and treatment,” etc. Research and review articles published in the past five to ten years were collected. The application of artificial intelligence in disease diagnosis, digital medicine, personalized medicine, drug discovery, and prognosis of epidemics is extensively reviewed in this article. Results: Deep learning and neural networks are the most widely used artificial intelligence technologies. Nonparametric Bayesian models are potential technologies for clinical trial design. Natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Conclusion: Deep learning and neural networks have been used in predicting the spread of seasonal influenza, Zika, Ebola, tuberculosis, and COVID-19. With the advancement of artificial intelligence technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research, as well as improved delivery of more effective and specialized services to the public.
Keywords
Artificial Intelligence, Medicine, Diagnosis and Treatment