Artificial Intelligence in Medicine and Imaging Applications

被引:0
作者
Rajpoot, Kuldeep [1 ]
机构
[1] Babulal Tarabai Inst Pharmaceut Sci, Dept Pharmaceut, Sagar 470228, Madhya Pradesh, India
关键词
Artificial intelligence; machine learning; deep learning; drug delivery; medical imaging; medical devices; black-box; NEURAL-NETWORKS; DIABETIC-RETINOPATHY; DECISION-SUPPORT; PREDICTION MODELS; RISK PREDICTION; IBM WATSON; DEVICES; SYSTEMS; CLASSIFICATION; VALIDATION;
D O I
10.2174/0113816128381171250415171256
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Artificial intelligence (AI) can completely transform drug development methods by delivering faster, more accurate, efficient results. However, the effective use of AI requires the accessibility of data of excellent quality, the resolution of ethical dilemmas, and an awareness of the drawbacks of AI-based techniques. Moreover, the application of AI in drug discovery is gaining popularity as an alternative to both the complex and time-consuming process of discovering as well as developing novel medications. Importantly, machine learning (ML) as well as natural language processing, for example, may boost both productivity as well as accuracy by analyzing vast volumes of data. This review article discusses in detail the promise of AI in drug discovery as well as offers insights into various topics such as societal issues related to the application of AI in medicine (e.g., legislation, interpretability and explainability, privacy and anonymity, and ethics and fairness), the importance of AI in the development of drug delivery systems, causability and explainability of AI in medicine, and opportunities and challenges for AI in clinical adoption, threat or opportunity of AI in medical imaging, the missing pieces of AI in medicine, approval of AI and ML-based medical devices.
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收藏
页数:19
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