{"title":"A Comprehensive Investigation: Developing the Pharmaceutical Industry through Artificial Intelligence.","authors":"Deepak Jain, Phool Chandra, Zeeshan Ali, Nishat Fatma, Hafsa Khan","doi":"10.2174/0115701638313233240830132804","DOIUrl":null,"url":null,"abstract":"<p><p>AI's rise has affected many aspects of civilization. Pharmaceutical businesses have been hit hard. This review paper highlights AI's benefits for disease detection, clinical trials, medicine development, and productivity in the pharmaceutical industry. Pharmaceutical companies have built specialized systems to help doctors diagnose and monitor medication remediation. Pharmaceutical businesses are utilizing AI for machine learning to imitate human analytical processes for more accurate and insightful data. AI has many benefits for the pharmaceutical business. Data analysis can address previously insoluble problems due to improved precision. AI boosts productivity, lowers expenses, and enhances tasks. AI insights enhance understanding of user behavior, market performance, and clinical trial success. AI helps identify patients during clinical trials and improves antiviral detection to ensure efficacy, safety, cost-effectiveness, and seamless pharmaceutical procedures. The pharmaceutical industry emphasizes AI in R&D, drug discovery, diagnostics, sickness prevention, epidemic forecasting, remote access, manufacturing, and marketing. Artificial intelligence has transformed medication development and discovery by analyzing vast datasets, discovering complicated patterns, and forecasting efficacy. Pharmaceutical companies like Novartis, AstraZeneca, and Verge Genomics are utilizing AI for drug feature prediction, neurological evaluation, therapy development, pulmonary and hypertension recognition, low-cost medication production, and disease diagnosis.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current drug discovery technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115701638313233240830132804","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
AI's rise has affected many aspects of civilization. Pharmaceutical businesses have been hit hard. This review paper highlights AI's benefits for disease detection, clinical trials, medicine development, and productivity in the pharmaceutical industry. Pharmaceutical companies have built specialized systems to help doctors diagnose and monitor medication remediation. Pharmaceutical businesses are utilizing AI for machine learning to imitate human analytical processes for more accurate and insightful data. AI has many benefits for the pharmaceutical business. Data analysis can address previously insoluble problems due to improved precision. AI boosts productivity, lowers expenses, and enhances tasks. AI insights enhance understanding of user behavior, market performance, and clinical trial success. AI helps identify patients during clinical trials and improves antiviral detection to ensure efficacy, safety, cost-effectiveness, and seamless pharmaceutical procedures. The pharmaceutical industry emphasizes AI in R&D, drug discovery, diagnostics, sickness prevention, epidemic forecasting, remote access, manufacturing, and marketing. Artificial intelligence has transformed medication development and discovery by analyzing vast datasets, discovering complicated patterns, and forecasting efficacy. Pharmaceutical companies like Novartis, AstraZeneca, and Verge Genomics are utilizing AI for drug feature prediction, neurological evaluation, therapy development, pulmonary and hypertension recognition, low-cost medication production, and disease diagnosis.