Harnessing the power of artificial intelligence in pharmaceuticals: Current trends and future prospects

Abstract

Introduction of artificial intelligence (AI) technology in the field of pharmaceutical industry has been driven to discovery and development of drugs, also personalized medicine. In this article The review investigates systematic trends facing AI-powered transformation. AI has improved efficiency by reducing the drug development time, costs and success rates due to machine learning (ML), deep learning (DL) and natural language processing (NLP). The literature search was conducted systematically, using core scientific databases to source data-mining research studies on predictive modelling, virtual screening, and automation in AI applications. Findings here underscore the critical role that AI plays in precision medicine, as well as process optimization in manufacture, but ethical issues and privacy of data and regulations add significantly to hurdles. The study confirms that AI presents unique opportunities for developing personalized healthcare and answering global health challenges, nonetheless its adoption involves overcoming ethical and regulatory issues beautiful collaboration and agreeing to industry wide standards. The next-generation products bring hope for low-cost, patient-centric solutions indicating pharmaceutical landscape phases of the paradigm.

Keywords

Artificial intelligence / Pharmaceuticals / Drug discovery / Predictive modeling / Precision medicine / Automation / Machine learning / Deep learning / Natural language processing

References

[1] Laermann-Nguyen U , Backfisch M . Innovation Crisis in the Pharmaceutical Industry? A Survey. SN Business & Economics; 2021.

[2] Sharma A , et al. A comprehensive review on strategies for new drug discovery and enhanced productivity in research and development:recent advancements and future prospectives. Mini-Reviews Org Chem. 2021May 1; 18 (3): 361- 382.

[3] Schlander M , Hernandez-Villafuerte K , Cheng CY , Mestre-Ferrandiz J , Baumann M . How much does it cost to research and develop a new drug? A systematic review and assessment. Pharmacoeconomics. 2021Nov; 39: 1243- 1269.

[4] Malandraki-Miller S , Riley PR . Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today. 2021Apr; 26 (4): 887- 901.

[5] Gholoom SI , Zervopoulos PD . The effect of mergers and acquisitions on efficiency:evidence from the pharmaceutical industry. Adv Sci Technol. 2023Sep 29; 129: 77- 95.

[6] Scannell JW , Bosley J , Hickman JA , et al. Predictive validity in drug discovery:what it is, why it matters and how to improve it. Nat Rev Drug Discov. 2022Dec; 21 (12): 915- 931.

[7] Vogel M , Kakani P , Chandra A , Conti RM . Medicare price negotiation and pharmaceutical innovation following the Inflation Reduction Act. Nat Biotechnol. 2024Mar; 42 (3): 406- 412.

[8] Blanco-Gonzalez A , Cabezon A , Seco-Gonzalez A , et al. The role of ai in drug discovery:challenges, opportunities, and strategies. Pharmaceuticals. 2023Jun 18; 16 (6): 891.

[9] Boniolo F , Dorigatti E , Ohnmacht AJ , Saur D , Schubert B , Menden MP . Artificial intelligence in early drug discovery enabling precision medicine. Expet Opin Drug Discov. 2021Sep 2; 16 (9): 991- 1007.

[10] Kiriiri GK , Njogu PM , Mwangi AN . Exploring different approaches to improve the success of drug discovery and development projects:a review. Future J Pharm Sci. 2020Dec; 6: 1- 2.

[11] Khan SR , Al Rijjal D , Piro A , Wheeler MB . Integration of AI and traditional medicine in drug discovery. Drug Discov Today. 2021Apr; 26 (4): 982- 992.

[12] Gupta R , Srivastava D , Sahu M , Tiwari S , Ambasta RK , Kumar P . Artificial intelligence to deep learning:machine intelligence approach for drug discovery. Mol Divers. 2021Aug; 25: 1315- 1360.

[13] Qureshi R , Irfan M , Gondal TM , et al. AI in drug discovery and its clinical relevance. Heliyon. 2023Jul; 9 (7): e17575.

[14] Kolluri S , Lin J , Liu R , Zhang Y , et al. Machine learning and artificial intelligence in pharmaceutical research and development:a review. AAPS J. 2022Jan 4; 24 (1): 19.

[15] Patel V , Shah M . Artificial intelligence and machine learning in drug discovery and development. Intell Med. 2022; 2: 134- 140.

[16] Mak KK , Wong YH , Pichika MR . Artificial intelligence in drug discovery and development. Drug Discovery and Evaluation:Safety and Pharmacokinetic Assays. 2023Sep 28 1- 38.

[17] Jiménez-Luna J , Grisoni F , Weskamp N , Schneider G . Artificial intelligence in drug discovery:recent advances and future perspectives. Expet Opin Drug Discov. 2021Sep 2; 16 (2): 949- 959.

[18] Pantin LP . Financial inclusion, cryptocurrency, and afrofuturism. Nw. UL Rev. 2023; 118 (3): 621- 659.

[19] Drobak JN . Rethinking Market Regulation:Helping Labor by Overcoming Economic Myths. 2021.

[20] Hodge Jr SD , Hubbard JE . COVID-19:the ethical and legal implications of medical rationing. Gonzaga Law Rev. 2020: 56 (159).

[21] Warburton CES . Economic Analysis and Law:The Economics of the Courtroom. 2020.

[22] Goshen Z , Levit D . Agents of inequality:common ownership and the decline of the American worker. Duke LJ. 2022; 72: 1- 69.

[23] Atkinson R . When communities design aid:creating solutions to poverty that people own. Use and Need. 2022.

[24] Levin Y . GOVERNING PRIORITIES.

[25] Martin MR . Bitcoin versus Fiat Currency:An Ethical Perspective. 2021.

[26] Vu TNN . Impact of Cryptocurrency on Traditional Financial Market. 2022.

[27] Jameaba MS . Digitalization, emerging technologies, and financial stability:challenges and opportunities for the Indonesian banking sector and beyondMuyanja Ssenyonga Jameaba. Qeios; 2023.

[28] Marks M . Biosupremacy:big data, antitrust, and monopolistic power over human behavior. UC Davis Law Rev. 2021: 55 (513).

[29] Drobak JN . Rethinking Market Regulation:Helping Labor by Overcoming Economic Myths. 2021.

[30] Jameaba MS . Digitalization, Emerging Technologies, and Financial Stability:Challenges and Opportunities for the Indonesian Banking Industry and beyond. 2022.

[31] Farghali H , Canová NK , Arora M . The potential applications of artificial intelligence in drug discovery and development. Physiol Res. 2021Dec 30; 70 (Suppl4): S715- S722.

[32] Rashid MB . Artificial intelligence effecting a paradigm shift in drug development. SLAS TECHNOLOGY:Transl Life Sci Innovat. 2021Feb; 26 (1): 3- 15.

[33] Maharana K , Mondal S , Nemade B . A review:data pre-processing and data augmentation techniques. In: Global Transitions Proceedings. 2022.

[34] Mchergui A , Moulahi T , Zeadally S . Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs). Veh Commun. 2022; 34 (3): 100403.

[35] Okoro ON , Hillman LA , Cernasev A . We get double slammed. Healthcare experiences of perceived discrimination among low-income African-American women. Women's Health. 2020Jan-Dec; 16, 1745506520953348.

[36] Yang Y , Zhuang Y , Pan Y . Multiple knowledge representation for big data artificial intelligence:framework, applications, and case studies. Front Information Technol Electron Eng. 2021Dec; 22 (12): 1551- 1558.

[37] Zhang L , Zhang L . Artificial intelligence for remote sensing data analysis:a review of challenges and opportunities. IEEE Geosci Rem Sens Mag. 2022Apr 13; 10 (2): 270- 294.

[38] Blasch E , Pham T , Chong CY , et al. Machine learning/artificial intelligence for sensor data fusion-opportunities and challenges. IEEE Aero Electron Syst Mag. 2021Jul 1; 36 (7): 80- 93.

[39] Salcedo-Sanz S , Ghamisi P , Piles M , et al. Machine learning information fusion in Earth observation:a comprehensive review of methods, applications and data sources. Inf Fusion. 2020Nov 1; 63: 256- 272.

[40] Agbehadji IE , Awuzie BO , Ngowi AB , Millham RC . Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. Int J Environ Res Publ Health. 2020Aug; 17 (15): 5330.

[41] Mishra S , Tyagi AK . The role of machine learning techniques in internet of thingsbased cloud applications. In: Artificial Intelligence-Based Internet of Things Systems. 2022.

[42] Minh D , Wang HX , Li YF , Nguyen TN . Explainable artificial intelligence:a comprehensive review. Artif Intell Rev. 2022; 55: 503- 3568.

[43] Unger JM , Hershman DL , Till C , et al. "When offered to participate":a systematic review and meta-analysis of patient agreement to participate in cancer clinical trials. JNCI:J Natl Cancer Inst. 2021Mar 1; 113 (3): 244- 257.

[44] von Dach E , Albrich WC , Brunel AS , et al. Effect of C-reactive protein-guided antibiotic treatment duration, 7-day treatment, or 14-day treatment on 30-day clinical failure rate in patients with uncomplicated gram-negative bacteremia:a randomized clinical trial. JAMA. 2020Jun 2; 323 (21): 2160- 2169.

[45] Mullens W , Dauw J , Martens P , et al. Acetazolamide in acute decompensated heart failure with volume overload. N Engl J Med. 2022Sep 29; 387 (13): 1185- 1195.

[46] Mazloomzadeh S , Khaleghparast S , Ghadrdoost B , et al. Effect of intermediate-dose vs standard-dose prophylactic anticoagulation on thrombotic events, extracorporeal membrane oxygenation treatment, or mortality among patients with COVID-19 admitted to the intensive care unit:the INSPIRATION randomized clinical trial. JAMA. 2021Apr 27; 325 (16): 1620- 1630.

[47] Lemos AC , do Espírito Santo DA , Salvetti MC , et al. Therapeutic versus prophylactic anticoagulation for severe COVID-19:a randomized phase II clinical trial (HESACOVID). Thromb Res. 2020Dec 1; 196: 359- 366.

[48] Jhund PS , Kondo T , Butt JH , et al. Dapagliflozin across the range of ejection fraction in patients with heart failure:a patient-level, pooled meta-analysis of DAPA-HF and DELIVER. Nat Med. 2022Sep; 28 (9): 1956- 1964.

[49] Moskowitz A , Huang DT , Hou PC , et al. Effect of ascorbic acid, corticosteroids, and thiamine on organ injury in septic shock:the ACTS randomized clinical trial. JAMA. 2020; Aug 18; 324 (7): 642- 650.

[50] Oyer RA , Hurley P , Boehmer L , et al. Increasing racial and ethnic diversity in cancer clinical trials:an American Society of Clinical Oncology and Association of Community Cancer Centers joint research statement. J Clin Oncol. 2022Jul 1; 40 (19): 2163- 2171.

[51] van der Veen A , Brenkman HJ , Seesing MF , et al. Laparoscopic versus open gastrectomy for gastric cancer (LOGICA):a multicenter randomized clinical trial. J Clin Oncol. 2021Mar; 20 (9): 978- 989.

[52] Bell G . TTalking to AI:an anthropological encounter with artificial intelligence. The SAGE Handbook of Cultural Anthropology. SAGE Publications Ltd.; 2021 Mar 31:442-458. vol. 1.

[53] van Assen M , Muscogiuri E , Tessarin G , De Cecco CN . Artificial intelligence:a century-old story. In: InArtificial Intelligence in Cardiothoracic Imaging. Cham:Springer International Publishing; 2022Apr 22: 3- 13.

[54] Lee RST , Lee RST . AI fundamentals. Artificial intelligence in daily life. 2020: 156.

[55] Luger GF , Luger GF . Modern AI and how we got hereKnowing our World:An Artificial Intelligence Perspective. 2021: 49- 74.

[56] Chatterjee R . Fundamental concepts of artificial intelligence and its applications. J Math Probl, Equ Stat. 2020; 1 (2): 13- 24.

[57] Kuipers M , Prasad R . Journey of artificial intelligence. Wireless Pers Commun. 2022; 123: 3275- 3290.

[58] Benbya H , Davenport TH , Pachidi S . Artificial intelligence in organizations:current state and future opportunities. MIS Q Exec. 2020; 19 (4): 9- 21.

[59] Dissanayake DMC . Artificial intelligence A brief overview of the discipline. 2021.

[60] Lee D , Yoon SN . Application of artificial intelligence-based technologies in the healthcare industry:opportunities and challenges. Int J Environ Res Publ Health. 2021Jan; 18 (1): 271.

[61] Amann J , Blasimme A , Vayena E , Frey D , Madai VI . Precise4Q Consortium. Exploitability for artificial intelligence in healthcare:a multidisciplinary perspective. BMC Med Inf Decis Making. 2020Dec; 20: 1- 9.

[62] Habli I , Lawton T , Porter Z . Artificial intelligence in health care:accountability and safety. Bull World Health Organ. 2020Apr 4; 98 (4): 251.

[63] Muehlematter UJ , Daniore P , Vokinger KN . Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20):a comparative analysis. The Lancet Digit Health. 2021Mar 1; 3 (3): e195- e203.

[64] Carter SM , Rogers W , Win KT , Frazer H , Richards B , Houssami N . The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast. 2020Feb 1; 49: 25- 32.

[65] Naik N , Hameed BM , Shetty DK , et al. Legal and ethical consideration in artificial intelligence in healthcare:who takes responsibility? Frontiers in surgery. 2022Mar 14; 9: 266.

[66] Gerke S , Minssen T , Cohen G . Ethical and legal challenges of artificial intelligencedriven healthcare. Artificial intelligence in healthcare. 2020: 295- 336.

[67] Wolff J , Pauling J , Keck A , Baumbach J . The economic impact of artificial intelligence in health care:systematic review. J Med Internet Res. 2020Feb 20; 22 (2): e16866.

[68] Benjamens S , Dhunnoo P , Pichika MR . Meskó B. The state of artificial intelligence-based FDAapproved medical devices and algorithms:an online database. NPJ Digit Med. 2020Sep 11; 3 (118).

[69] Safdar NM , Banja JD , Meltzer CC . Ethical considerations in artificial intelligence. European J Radiol. 2020Jan; 122: 108768.

[70] Meszaros J , Minari J , Huys I . The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Front Genet. 2022Oct 4; 13: 927721.

[71] Tzanou M . Personal Data Protection and Legal Developments in the European Union. 2020.

[72] Forti M . The deployment of artificial intelligence tools in the health sector:privacy concerns and regulatory answers within the GDPR. Eur J Legal Stud. 2021; 13 (1): 29- 44.

[73] Sartor G , Lagioia F . The impact of the general data protection regulation (GDPR) on artificial intelligenc. 2020.

[74] Bak M , Madai VI , Fritzsche MC , Mayrhofer MT , McLennan S . You can't have ai both ways:balancing health data privacy and access fairly. Front Genet. 2022Jun 13; 13: 929453.

[75] Hansen J , Wilson P , Verhoeven E , et al. Assessment of the EU Member States' rules on health data in the light of GDPR.

[76] Mourby M , Cathaoir K , Collin CB . Transparency of machine-learning in healthcare:the GDPR & European health law. Comput Law Secur Rep. 2021; 43 (105611).

[77] Estevez-Ordonez D , Abdelrashid M , Coffee E , et al. Racial and socioeconomic disparities in glioblastoma outcomes:a single-center, retrospective cohort study. Cancer. 2023Oct 1; 129 (19): 3010- 3022.

[78] Harper LJ , Kidambi P , Kirincich JM , Thornton JD , Khatri SB , Culver DA . Health disparities:interventions for pulmonary disease-A narrative review. Chest. 2023Feb 27 Jul; 164 (1): 179- 189.

[79] Snowden LR , Graaf G , Keyes L , Kitchens K , Ryan A , Wallace N . Did Medicaid expansion close African American-white health care disparities nationwide? A scoping review. BMC Publ Health. 2022Aug 30; 22 (1): 1638.

[80] Ketchum FB , Erickson CM , Chin NA , et al. What influences the willingness of Blacks and African Americans to enroll in preclinical Alzheimer's disease biomarker research? A qualitative vignette analysis. J Alzheim Dis. 2022Jan 1; 87 (3): 1167- 1179.

[81] Hagiwara N , Duffy C , Quillin J . Implicit and explicit racial prejudice and stereotyping toward Black (vs. White) Americans:the prevalence and variation among genetic counselors in North America. J Genet Counsel. 2023Apr; 32 (2): 397- 410.

[82] Chowdhury-Paulino IM , Ericsson C , Vince Jr R , Spratt DE , George DJ , Mucci LA . Racial disparities in prostate cancer among black men:epidemiology and outcomes. Prostate Cancer Prostatic Dis. 2022Sep; 25 (3): 397- 402.

[83] Geneviève LD , Martani A , Wangmo T , Elger BS . Precision public health and structural racism in the United States:promoting health equity in the COVID-19 pandemic response. JMIR Publ Health Surveill. 2022Mar 4; 8 (3): e33277.

[84] Liu SR , Glynn LM . The contribution of racism-related stress and adversity to disparities in birth outcomes:evidence and research recommendations. F&S Reports. 2022Nov; 3 (2 Suppl): 5- 13.

[85] Kordzadeh N , Ghasemaghaei M . Algorithmic bias:review, synthesis, and future research directions. Eur J Inf Syst. 2022May 4; 31 (3): 388- 409.

[86] Daneshjou R , Smith MP , Sun MD , Rotemberg V , Zou J . Lack of transparency and potential bias in artificial intelligence data sets and algorithms:a scoping review. JAMA Dermatol. 2021Nov 1; 157 (11): 1362- 1369.

[87] Noseworthy PA , Attia ZI , Brewer LC , et al. Assessing and mitigating bias in medical artificial intelligence:the effects of race and ethnicity on a deep learning model for ECG analysis. Circ:Arrhythm Electrophysiol. 2020Mar; 13 (3): e007988.

[88] Seyyed-Kalantari L , Zhang H , McDermott MB , Chen IY , Ghassemi M . Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021Dec; 27 (12): 2176- 2182.

[89] Gerke S . Health AI for good rather than evil? The need for a new regulatory framework for AI-based medical devices. Yale J. Health Pol'y L. & Ethics. 2021; 20 (2): 433.

[90] Harvey HB , Gowda V . How the FDA Regulates AI. Academic radiology; 2020.

[91] Bretthauer M , Gerke S , Hassan C , Ahmad OF , Mori Y . The new European medical device regulation:balancing innovation and patient safety. Ann Intern Med. 2023Jun; 176 (6): 844- 848.

[92] Zanca F , Brusasco C , Pesapane F , Kwade Z , Beckers R , Avanzo M . Regulatory aspects of the use of artificial intelligence medical software. In: InSeminars in Radiation Oncology. vol. 32. WB Saunders; 2022Oct 1: 432- 441, 4.

[93] Higgins DC , Johner C . Validation of artificial intelligence containing products across the regulated healthcare industries. Therapeutic Innovation & Regulatory Science. 2023Jul; 57 (4): 797- 809.

[94] Sharma K , Manchikanti P . Regulation of artificial intelligence in drug discovery and health care. Biotechnol Law Rep. 2020; 39 (5).

[95] Johnson WG . Flexible regulation for dynamic products? The case of applying principles-based regulation to medical products using artificial intelligence. Law.

[96] Liang NL , Chung TK , Vorp DA . The regulatory environment for artificial intelligence enabled devices in the United States. Semin Vasc Surg. 2023Sep; 36 (3): 435- 439.

[97] Vatansever S , Schlessinger A , Wacker D , et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases:state-of-the-arts and future directions. Med Res Rev. 2021May; 41 (3): 1427- 1473.

[98] Sarkar C , Das B , Rawat VS , et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. 2023Jan 19; 24 (3): 2026.

[99] Dara S , Dhamercherla S , Jadav SS , Babu CM , Ahsan MJ . Machine learning in drug discovery:a review. Artif Intell Rev. 2022Mar; 55 (3): 1947- 1999.

[100] Sadybekov AV , Katritch V . Computational approaches streamlining drug discovery. Nature. 2023Apr; 616 (7958): 673- 685.

[101] Gautam V , Gaurav A , Masand N , Lee VS , et al. Artificial intelligence and machinelearning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers. 2023Apr; 27 (2): 959- 985.

[102] Zhao L , Ciallella HL , Aleksunes LM , Zhu H . Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today. 2020Sep; 25 (9): 1624- 1638.

[103] Mouchlis VD , Afantitis A , Serra A , et al. Advances in de novo drug design:from conventional to machine learning methods. Int J Mol Sci. 2021Feb 7; 22 (4): 1676.

[104] Shaker B , Ahmad S , Lee J , Jung C , Na D . In silico methods and tools for drug discovery. Comput Biol Med. 2021Oct 1; 137: 104851.

[105] Carracedo-Reboredo P , Liñares-Blanco J , Rodríguez-Fernández N , et al. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J. 2021Jan 1; 19: 4538- 4558.

[106] Jarada TN , Rokne JG , Alhajj R . A review of computational drug repositioning:strategies, approaches, opportunities, challenges, and directions. J Cheminf. 2020Jul 22; 12 (1): 46.

[107] Lin X , Li X , Lin X . A review on applications of computational methods in drug screening and design. Molecules. 2020; 25 (6): 1375, 18.

[108] Cui W , Aouidate A , Wang S , Yu Q , Li Y , Yuan S . Discovering anti-cancer drugs via computational methods. Front Pharmacol. 2020May 20; 11: 733.

[109] Alanazi SA , Kamruzzaman MM , Alruwaili M , Alshammari N , Alqahtani SA , Karime A . Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. J Healthc Eng. 2020: 2020.

[110] Selvaraj C , Chandra I , Singh SK . Artificial intelligence and machine learning approaches for drug design:challenges and opportunities for the pharmaceutical industries. Mol Divers. 2021Jun; 26 (3): 1893- 1913.

[111] Meenakshi DU , Nandakumar S , Francis AP , et al. Deep learning and site-specific drug delivery:the future and intelligent decision support for pharmaceutical manufacturing science. Deep Learning for Targeted Treatments:Transformation in Healthcare. 2022Sep; 16: 1- 38.

[112] Walters WP , Barzilay R . Applications of deep learning in molecule generation and molecular property prediction. Acc Chem Res. 2020; 54 (2): 263- 270.

[113] Bannigan P , Aldeghi M , Bao Z , Häse F , Aspuru-Guzik A , Allen C . Machine learning directed drug formulation development. Adv Drug Deliv Rev. 2021Aug 1; 175: 113806.

[114] Weissler EH , Naumann T , Andersson T , et al. The role of machine learning in clinical research:transforming the future of evidence generation. Trials. 2021Dec; 22: 1- 5.

[115] Patel L , Shukla T , Huang X , Ussery DW , et al. Machine learning methods in drug discovery. Molecules. 2020; 25 (22): 5277, 12.

[116] Elbadawi M , Gaisford S , Basit AW . Advanced machine-learning techniques in drug discovery. Drug Discov Today. 2021Mar; 26 (3): 769- 777.

[117] Kumar Attar R , Komal . The emergence of Natural Language Processing (NLP) techniques in healthcare AI. In: InArtificial Intelligence for Innovative Healthcare Informatics. Cham:Springer International Publishing; 2022May 24: 285- 307.

[118] Liu S , Wang X , Hou Y , et al. Multimodal data matters:language model pre-training over structured and unstructured electronic health records. IEEE J Biomed Health Informatics. 2022Oct 28; 27 (1): 504- 514.

[119] Zhou B , Yang G , Shi Z , Ma S . Natural language processing for smart healthcare. IEEE Rev Biomed Eng. 2022Sep; 28 (17): 4- 18.

[120] Muralidharan C , Anitha R . Patient report analysis for identification and diagnosis of disease. Machine Learning for Healthcare. 2020: 129- 158.

[121] Aladakatti SS , Senthil Kumar S . Exploring natural language processing techniques to extract semantics from unstructured dataset which will aid in effective semantic interlinking. Int J Model, Simulat, Sci Comput. 2023Feb 28; 14 (1): 2243004.

[122] Baviskar D , Ahirrao S , Potdar V , Kotecha K . Efficient automated processing of the unstructured documents using artificial intelligence:a systematic literature review and future directions. IEEE Access. 2021. PP (99): 1- 1.

[123] Cherukuri LS . Advancing personalized medicine:a strategic framework for integrating NON-traditional data sources. J Comput Eng Technol (IJCET). 2024; 15 (1): 7- 15.

[124] Banerjee PS , Chakraborty B , Anand U , Upadhyay H . Trainable framework for information extraction, structuring and summarization of unstructured data, using modified NER. Wireless Pers Commun. 2021Mar; 117 (2): 769- 807.

[125] Fang Y . Design and Realization of Database System for Judgement Documents Based on Natural Language Processing. Database. 2012, 6- 1.

[126] Rajesh J , Babu PC . Significance of natural language processing in data analysis using business intelligence. In: InDeep Natural Language Processing and AI Applications for Industry 5.0. IGI Global; Drug Discov Today. 2021: 169- 188.

[127] Haleem A , Javaid M , Singh RP , Suman R . Medical 4.0 technologies for healthcare:features, capabilities, and applications. Internet of Things and Cyber-Phys Syst. 2022Jan 1; 2: 12- 30.

[128] Khang A , Abdullayev V , Hrybiuk O , Shukla AK . Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. 2024.

[129] Khang A , Abdullayev V , Ali RN , Bali SY , Mammadaga GM , Hafiz MK . Using big data to solve problems in the field of medicine. In: InComputer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press; 2024: 407- 418.

[130] Sharma A , Kaur J , Singh I . Internet of things (IoT) in pharmaceutical manufacturing, warehousing, and supply chain management. SN Comput Sci. 2020; 1 (232).

[131] Zakari N , Al-Razgan M , Alsaadi A , et al. Blockchain technology in the pharmaceutical industry:a systematic review. PeerJ Comput Sci. 2022Mar 11; 8: e840.

[132] Khang A . AI and IoT-Based Technologies for Precision Medicine. Drug Discov Today. 2023.

[133] Rayhan A , Rayhan R , Rayhan S . The Role Of AI In Healthcare:Revolutionizing Patient Care And Well-Being. .

[134] Ghazal TM , Alshurideh MT , Alzoubi HM . Blockchain-enabled internet of things (IoT) platforms for pharmaceutical and biomedical research. In: The International Conference on Artificial Intelligence and Computer Vision. vol. 29. Cham:Springer International Publishing; 2021May: 589- 600.

[135] Khang A , Rana G , Tailor RK , Abdullayev V . Data-centric AI solutions and emerging technologies in the healthcare ecosystem. 2023.

[136] Svoboda P , Ghazal TM , Afifi MA , Kalra D , Alshurideh MT , Alzoubi HM . Information systems integration to enhance operational customer relationship management in the pharmaceutical industry. InThe International Conference on Artificial Intelligence and Computer Vision. Cham:Springer International Publishing; 2021May 29: 553- 572.

[137] Ghazal TM , Hasan MK , Alshurideh MT , et al. IoT for smart cities:machine learning approaches in smart healthcare-a review. Future Internet. 2021Aug 23; 13 (8): 218.

[138] Khang A , Abdullayev V , Hrybiuk O , Shukla AK . Computer vision and AI-integrated IoT technologies in the medical ecosystem. 2024.

[139] Bashir AK , Victor N , Bhattacharya S , et al. Federated learning for the healthcare metaverse:concepts, applications, challenges, and future directions. IEEE Internet Things J. 2023Aug 14; PP (99).

[140] Sebastian AM , Peter D . Artificial intelligence in cancer research:trends, challenges and future directions. Life. 2022Nov 28; 12 (12): 1991.

[141] Facchinetti G , Petrucci G , Albanesi B , De Marinis MG , Piredda M . Can smart home technologies help older adults manage their chronic condition? A systematic literature review. Int J Environ Res Publ Health. 2023Jan 10; 20 (2): 1205.

[142] Kumar S , Underwood SH , Masters JL , et al. Ten questions concerning smart and healthy built environments for older adults. Build Environ. 2023Oct 1; 244: 110720.

[143] Agrebi S , Larbi A . Use of artificial intelligence in infectious diseases. Artificial intelligence in precision health. 2020: 415- 438.

[144] Xu Y , Liu X , Cao X , et al. Artificial intelligence:a powerful paradigm for scientific research. Innovation. 2021Nov 28; 2 (4).

[145] Srivani M , Murugappan A , Mala T . Cognitive computing technological trends and future research directions in healthcare-a systematic literature review. Artif Intell Med. 2023; 138: 102513.

[146] Pradhan B , Bharti D , Chakravarty S , et al. Internet of things and robotics in transforming current-day healthcare services. J Healthc Eng. 2021May 26; 2021: 1- 5.

[147] Ahmed Z , Mohamed K , Zeeshan S , Dong XQ . Artificial intelligence with multifunctional machine learning platform development for better healthcare and precision medicine. Database. 2020, 1;2020:baaa010.

[148] Sollini M , Bartoli F , Marciano A , Zanca R , Slart RH , Erba PA . Artificial intelligence and hybrid imaging:the best match for personalized medicine in oncology. European J Hybrid Imaging. 2020Dec; 4: 1- 22.

[149] Quazi S . Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022; 39 (8): 120, 15.

[150] Vadapalli S , Abdelhalim H , Zeeshan S , Ahmed Z . Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Briefings Bioinf. 2022Sep; 23 (5): bbac191.

[151] Hassan M , Awan FM , Naz A , et al. Innovations in genomics and big data analytics for personalized medicine and health care:a review. Int J Mol Sci. 2022Apr 22; 23 (9): 4645.

[152] Bhinder B , Gilvary C , Madhukar NS , Elemento O . Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021; 11 (4): 900- 915.

[153] Jhawat V , Gupta S , Gulia M , Nair A . Artificial intelligence and data science in pharmacogenomics-based drug discovery:future of medicines. Data Science for Genomics. 2023: 85- 97.

[154] Dlamini Z , Francies FZ , Hull R , Marima R . Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020Jan 1; 18: 2300- 2311.

[155] Hamamoto R , Suvarna K , Yamada M , et al. Application of artificial intelligence technology in oncology:towards the establishment of precision medicine. Cancers. 2020Nov 26; 12 (12): 3532

[156] Singh AV , Chandrasekar V , Paudel N , et al. Integrative toxicogenomics:advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother. 2023Jul 1; 163: 114784.

[157] Vora LK , Gholap AD , Jetha K , Thakur RR , Solanki HK , Chavda VP . Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023Jul 10; 15 (7): 1916.

[158] Alshawwa SZ , Kassem AA , Farid RM , Mostafa SK , Labib GS . Nanocarrier drug delivery systems:characterization, limitations, future perspectives and implementation of artificial intelligence. Pharmaceutics. 2022Apr 18; 14 (4): 883.

[159] Gao J , Karp JM , Langer R , Joshi N . The future of drug delivery. Chem Mater. 2023; 35 (2): 359- 363, 24.

[160] Sharma T , Mankoo A , Sood V . Artificial intelligence in advanced pharmacy. Int J Sci Res Archive. 2021; 2 (1): 47- 54.

[161] Wang W , Ye Z , Gao H , Ouyang D . Computational pharmaceutics-A new paradigm of drug delivery. J Contr Release. 2021; 10 (338): 119- 136.

[162] Arden NS , Fisher AC , Tyner K , Lawrence XY , Lee SL , Kopcha M . Industry 4.0 for pharmaceutical manufacturing:preparing for the smart factories of the future. Int J Pharm. 2021Jun 1; 602: 120554.

[163] Kulkov I . The role of artificial intelligence in business transformation:a case of pharmaceutical companies. Technol Soc. 2021; 66: 101629.

[164] Serov N , Vinogradov V . Artificial intelligence to bring nanomedicine to life. Adv Drug Deliv Rev. 2022; 184: 114194.

[165] Egorov E , Pieters C , Korach-Rechtman H , Shklover J , Schroeder A . Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems. Drug Delivery Translat Res. 2021Apr; 11: 345- 352.

[166] Fruehwirt W , Duckworth P . Towards better healthcare:what could and should be automated? Technol Forecast Soc Change. 2021; 172: 120967.

[167] Tursunbayeva A , Renkema M . Artificial intelligence in health-care:implications for the job design of healthcare professionals. Asia Pac J Hum Resour. 2023Oct; 61 (4): 845- 887.

[168] Bohr A , Memarzadeh K . The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in healthcare. 2020: 25- 60.

[169] Asan O , Choudhury A . Research trends in artificial intelligence applications in human factors health care:mapping review. JMIR human factors. 2021; 8 (2): e28236, 18.

[170] Vallès-Peris N , Barat-Auleda O , Domènech M . Robots in healthcare? What patients say. Int J Environ Res Publ Health. 2021Sep 21; 18 (18): 9933.

[171] Willis M , Duckworth P , Coulter A , Meyer ET , Osborne M . Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice. BMJ Open. 2020Jun 1; 10 (6): e032412.

[172] Alowais SA , Alghamdi SS , Alsuhebany N , et al. Revolutionizing healthcare:the role of artificial intelligence in clinical practice. BMC Med Educ. 2023Sep 22; 23 (1): 689.

[173] Ilan Y . Improving global healthcare and reducing costs using second-generation artificial intelligence-based digital pills:a market disruptor. Int J Environ Res Publ Health. 2021Jan; 18 (2): 811.

[174] Chopra H , Baig AA , Gautam RK , Kamal MA . Application of artificial intelligence in drug discovery. Curr Pharmaceut Des. 2022Sep 1; 28 (33): 2690- 2703.

[175] Bhattamisra SK , Banerjee P , Gupta P , Mayuren J , Patra S , Candasamy M . Artificial intelligence in pharmaceutical and healthcare research. Big Data and Cognitive Computing. 2023Jan 11; 7 (1): 10.

[176] Chen Z , Liu X , Hogan W , Shenkman E , et al. Applications of artificial intelligence in drug development using real-world data. Drug Discov Today. 2021; 26 (5): 1256- 1264.

[177] Tripathi N , Goshisht MK , Sahu SK , Arora C . Applications of artificial intelligence to drug design and discovery in the big data era:a comprehensive review. Mol Divers. 2021; 25 (3): 1643- 1664.

[178] Leite ML , de Loiola Costa LS , Cunha VA , et al. Artificial intelligence and the future of life sciences. Drug Discov Today. 2021Nov 1; 26 (11): 2515- 2526.

[179] Bao C , Singh H , Meyer B , Kirksey K , et al. Patient-provider engagement and its impact on health outcomes:a longitudinal study of patient portal use. MIS Q.2. 2020: 44.

[180] Ross EL , Jamison RN , Nicholls L , Perry BM , Nolen KD . Clinical integration of a smartphone app for patients with chronic pain:retrospective analysis of predictors of benefits and patient engagement between clinic visits. J Med Internet Res. 2020Apr 16; 22 (4): e16939.

[181] Wei Y , Zheng P , Deng H , Wang X , Li X , Fu H . Design features for improving mobile health intervention user engagement:systematic review and thematic analysis. J Med Internet Res. 2020Dec 9; 22 (12): e21687.

[182] Iribarren SJ , Akande TO , Kamp KJ , Barry D , Kader YG , Suelzer E . Effectiveness of mobile apps to promote health and manage disease:systematic review and metaanalysis of randomized controlled trials. JMIR mHealth and uHealth. 2021Jan 11; 9 (1): e21563.

[183] Spaulding EM , Marvel FA , Piasecki RJ , Martin SS , Allen JK . User engagement with smartphone apps and cardiovascular disease risk factor outcomes:systematic review. JMIR cardio. 2021Feb 3; 5 (1): e18834.

[184] Lyles CR , Nelson EC , Frampton S , Dykes PC , Cemballi AG , Sarkar U . Using electronic health record portals to improve patient engagement:research priorities and best practices. Ann Intern Med. 2020Jun 2; 172 (11_Supplement): S123- S129.

[185] Böhm AK , Jensen ML , Sørensen MR , Stargardt T . Real-world evidence of user engagement with mobile health for diabetes management:longitudinal observational study. JMIR mHealth and uHealth. 2020Nov 6; 8 (11): e22212.

[186] Liu K , Xie Z , Or CK , et al. Of mobile app-assisted self-care interventions for improving patient outcomes in type 2 diabetes and/or hypertension:systematic review and meta-analysis of. JMIR mHealth and uHealth. 2020; 8 (8): e15779.

[187] Ghose A , Guo X , Li B , Dang Y . Empowering patients using smart mobile health platforms:evidence from a randomized field experiment. arXiv preprint arXiv:2102.05506. 2021.

[188] Amjad A , Kordel P , Fernandes G . A review on innovation in healthcare sector (telehealth) through artificial intelligence. Sustainability. 2023; 15 (6655): 1- 24.

[189] Baum ZJ , Yu X , Ayala PY , Zhao Y , Watkins SP , Zhou Q . Artificial intelligence in chemistry:current trends and future directions. J Chem Inf Model. 2021Jul 15; 61 (7): 3197- 3212.

[190] Ghaffar Nia N , Kaplanoglu E , Nasab A . Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell. 2023; 3 (5).

[191] Makimoto H , Kohro T . Adopting artificial intelligence in cardiovascular medicine:a scoping review. Hypertens Res. 2024; 47 (3): 685- 699.

[192] Albahri AS , Duhaim AM , Fadhel MA , et al. A systematic review of trustworthy and explainable artificial intelligence in healthcare:assessment of quality, bias risk, and data fusion. Inf Fusion. 2023Mar 15; 96: 156- 191.

[193] Li Z , Koban KC , Schenck TL , Giunta RE , Li Q , Sun Y . Artificial intelligence in dermatology image analysis:current developments and future trends. J Clin Med. 2022Nov 18; 11 (22): 6826.

[194] Joksimovic S , Ifenthaler D , Marrone R , De Laat M , Siemens G . Opportunities of artificial intelligence for supporting complex problem-solving:findings from a scoping review. Comput Educ: Artif Intell. 2023May 1: 100138.

[195] Kumar M , Nguyen TN , Kaur J , et al. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep. 2023Feb; 75 (1): 3- 18.

[196] Kakhi K , Alizadehsani R , Kabir HD , Khosravi A , Nahavandi S , Acharya UR . The internet of medical things and artificial intelligence:trends, challenges, and opportunities. Biocybern Biomed Eng. 2022Jul 1; 42 (3): 749- 771.

[197] Davis AM , Engkvist O , Fairclough RJ , Feierberg I , Freeman A , Iyer P . Public-private partnerships:compound and data sharing in drug discovery and development. SLAS DISCOVERY: Adv Sci Drug Discov. 2021Jun; 26 (5): 604- 619.

[198] Romasanta AK , van der Sijde PC , Smit MJ , de Esch IJ , Jahnke W , van MuijlwijkKoezen JE . Career development in fragment-based drug discovery. Drug Discov Today Technol. 2020Dec 1; 37: 107- 116.

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