<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.</p>
</abstract>

Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

Department of Prosthodontics, Manipal College of Dental Sciences

Department of Oral Pathology and Microbiology, MR Ambedkar Dental College and Hospital

Division of Pharmacology, Department of Basic Medical Sciences

Department of Oral Pathology and Microbiology, Manipal College of Dental Sciences

Acta Marisiensis - Seria Medica

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of...

Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

Published Online: Apr 03, 2024

Page range: 8 - 15

Received: Aug 24, 2023

Accepted: Jan 11, 2024

DOI: https://doi.org/10.2478/amma-2024-0002

Keywords
artificial intelligence, machine learning, deep learning, drug discovery, clinical trials, precision medicine

© 2024 B V Swapna et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

Published Online: Apr 03, 2024

Page range: 8 - 15

Received: Aug 24, 2023

Accepted: Jan 11, 2024

DOI: https://doi.org/10.2478/amma-2024-0002

Keywordsartificial intelligence, machine learning, deep learning, drug discovery, clinical trials, precision medicine

© 2024 B V Swapna et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
artificial intelligence, machine learning, deep learning, drug discovery, clinical trials, precision medicine