Mining of molecular insights of CYP2A6 and its variants complex with coumarin (CYP2A6*-coumarin) using molecular dynamics simulation

Yadav, Anamika; Kesharwani, Akanksha; Chaurasia, Dheeraj Kumar; Katara, Pramod

doi:10.1080/07391102.2022.2062785

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…Here, we cover the potential applications of AI in healthcare and the barriers to broader use. Many tools have been used to screen the model, but they are using various algorithms that can also be implemented in ML for better training and testing and can be converted to the various healthcare sectors [20, 21 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Application Areas Of Ai In Healthcarementioning

confidence: 99%

“…It is based on training the model with provided data and predicting its correctness by testing it with anonymous data. Deloitte found that 63% of the 1,100 US businesses utilising AI concentrated on ML [ 28 , 30 , 32 ]. The industry's organizational side may benefit from the application of ML.…”

Section: Potential Of ML In Healthcarementioning

confidence: 99%

“…Frequently, a patient's needs transcend beyond their present physical conditions. The right course of action for a certain patient might vary depending on their social, economic, and historical conditions [ 28 , 42 , 55 , 57 ]. For instance, an AI system could be able to place a patient in a certain care facility based on a specific diagnosis.…”

Section: Challenges Of ML In Medical Sciencesmentioning

confidence: 99%

“…In ML, an area of AI, the system is taught to learn from data. ML uses an algorithm that helps the software application predict outcomes without explicitly programming [ 28 , 42 , 55 , 57 ]. The algorithms utilise data to predict outcomes based on patterns.…”

Section: Challenges Of ML In Medical Sciencesmentioning

confidence: 99%

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Current trends on the application of artificial intelligence in medical sciences

Mashraqi¹

2022

Bioinformation

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Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.

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