Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy

Chintawar, Sanket; Gattani, Vaishnavi; Vyas, Shivanee; Dawre, Shilpa

doi:10.1002/9781119865728.ch14

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…Quantitative structure-activity relationship (QSAR) is a computational approach that utilizes the chemical structure of a molecule to predict its biological activity. This method has found application in pharmacokinetics, where it can be employed to anticipate drug ADME properties, including solubility, permeability, and metabolism (Figure 7) [121,[188][189][190][191]. A pharmacodynamic study includes the drug's effect on the target.…”

Section: Ai-based Methods To Predict Pharmacokinetic Parametersmentioning

confidence: 99%

“…Overall, AI-based models provide a powerful tool for predicting drug release and absorption parameters. By analyzing various factors and leveraging machine learning algorithms, these models can optimize drug formulations, guide drug development decisions, and contribute to the design of more effective drug delivery systems [ 189 , 190 , 191 , 192 , 193 , 194 , 197 ].…”

Section: Ai For Pharmacokinetics and Pharmacodynamicsmentioning

confidence: 99%

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Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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Section: Ai-based Methods To Predict Pharmacokinetic Parametersmentioning

confidence: 99%

Section: Ai For Pharmacokinetics and Pharmacodynamicsmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

View full text Add to dashboard Cite

show abstract

“…In supervised Learning, the algorithm is trained on a labeled dataset and generalizes its learnings from the labeled 1.3 SENSOR-BASED SOLUTIONS FOR OLDER ADULT CARE 1 9 examples for prediction on the unlabeled new data [62]. For example, a supervised learning algorithm is trained to identify patterns in medical images (such as X-rays or magnetic resonance imaging scans) for the diagnosis of diseases (such as cancer or pneumonia) [63]. In unsupervised learning, the algorithm is trained on an unlabeled dataset, allowing it to find patterns or structures in the data without being explicitly guided [64].…”

Section: Computing Unit: Machine Learningmentioning

confidence: 99%

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

Sharma

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Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy

Cited by 2 publications

References 48 publications

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

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