Artificial Intelligence in Stroke – A Review

patil, Anushri; patil, Shubhangi

doi:10.47750/pnr.2022.13.s06.414

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…To ensure the ethical and successful application of AI in pharmacokinetics and pharmacodynamics, it is essential to overcome difficulties relating to explainability, data sharing, regulatory norms, and clinical translation. 36…”

Section: Future Paths To Take and Opportunities To Seize Regarding Th...mentioning

confidence: 99%

Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review

Pawar¹,

Patil²,

Tamboli³

et al. 2023

IJPQA

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Personalized medicine, medication discovery, and development might all benefit greatly from AI’s incorporation into pharmacokinetics and pharmacodynamics. Target identification, therapeutic effectiveness prediction, drug design optimization, obstacles, and future possibilities are all explored in this survey of AI applications in these areas. An overview of pharmacokinetics and pharmacodynamics is presented first, stressing the significance of knowing how drugs are absorbed, distributed, metabolized, and excreted and the correlation between drug concentration and pharmacological effect. The article then looks into the function of AI in target identification, exploring how machine learning algorithms and data integration may be used to discover new drug targets and enhance the design of existing ones. Classification and regression methods are also investigated for their potential use in the prediction of therapeutic efficacy using AI. Patient data, molecular interaction data, and clinical response data are just a few examples of the types of data that may be used to fuel the creation of predictive models that might assist in dosage and efficacy optimization. Metrics and procedures for validating these models are addressed to evaluate their efficacy. Additionally, de novo drug design, virtual screening, and structure-based drug design are all discussed in relation to the use of AI in optimizing drug development. The paper provides examples of how AI has been applied successfully in different settings, demonstrating its potential to hasten the drug discovery process and enhance treatment outcomes. We examine data availability, interpretability, and ethical implications as challenges and limits of AI in pharmacokinetics and pharmacodynamics. To guarantee these technologies’ proper and ethical use, we also discuss the regulatory elements and rules for applying AI in drug research. Possibilities and prospects for the use of AI in pharmacokinetics and pharmacodynamics are discussed as a conclusion to the review. It stresses the significance of regulatory standards and clinical translation, as well as the incorporation of multiomics data, deep learning methods, real-time monitoring, explainable artificial intelligence, collaborative networks, and more.

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Section: Future Paths To Take and Opportunities To Seize Regarding Th...mentioning

confidence: 99%

Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review

Pawar¹,

Patil²,

Tamboli³

et al. 2023

IJPQA

View full text Add to dashboard Cite

show abstract

“…Nanotechnology-based detection method with 98.5% accuracy for the CRNN classifier model and 96.5% accuracy for the BoVW classifier model. Sumathi C et al [14]; proposed "Medical Imaging with Artificial Intelligence for Lung Disease Analysis: A Comprehensive Review". In this article, the model is built using the CNN, VGG16, VGG19, and ResNet 50 algorithms, and two datasets from the Kaggle repository are utilised.…”

Section: B Non-small Cell Lung Cancer(nsclc)mentioning

confidence: 99%

Lung Cancer Detection and Classification using Machine Learning Algorithms

Mishra,

Gangwar

2023

IJRITCC

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Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by gathering the dataset from the survey and applying machine learning methods such as Support Vector Machine, Nave Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest. With this result, it is revealed that Decision Tree attained the maximum accuracy of 100% as compared to the others.

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Artificial Intelligence in Stroke – A Review

Cited by 2 publications

References 31 publications

Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review

Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review

Lung Cancer Detection and Classification using Machine Learning Algorithms

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