AI has emerged as a revolutionary technology in the pharmaceutical and biomedical fields. This review explores its transformative role, particularly in drug development, the discovery of future interventions in the pharmaceutical sector. By leveraging AI, these processes have become more efficient, cost-effective, and capable of delivering personalized medicine to individual patients. Moreover, AI’s potential in disease prevention and outbreak prediction is promising, as it can analyze vast datasets to identify crucial patterns and trends, leading to targeted interventions for combating diseases. In biomedical research, AI has proven highly beneficial, especially in genomics, proteomics, and metabolomics, where it enables researchers to comprehensively analyze complex biological data, uncovering new insights and accelerating scientific discoveries. The impact of AI is also evident in the patient-physician interface, as it enhances diagnostic accuracy and treatment efficiency, ultimately improving patient care.
A. Zhavoronkov, Q. Vanhaelen, T.I. Oprea. Will artificial intelligence for drug discovery impact clinical pharmacology. Clinical Pharmacology &Therapeutics, 2020, 107(4): 780−785. https://doi.org/10.1002/cpt.1795
S. Vatansever, A. Schlessinger, D. Wacker, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Medicinal Research Reviews, 2021, 41(3): 1427−1473. https://doi.org/10.1002/med.21764
E.H. Weissler, T. Naumann, T. Andersson, et al. The role of machine learning in clinical research: Transforming the future of evidence generation. Trials, 2021, 22(1): 537. https://doi.org/10.1186/s13063-021-05489-x
K.K. Mak, M.K. Balijepalli, M.R. Pichika. Success stories of AI in drug discovery - where do things stand. Expert Opinion on Drug Discovery, 2022, 17(1): 79−92. https://doi.org/10.1080/17460441.2022.1985108
R. Dias, A. Torkamani. Artificial intelligence in clinical and genomic diagnostics. Genome Medicine, 2019, 11: 70. https://doi.org/10.1186/s13073-019-0689-8
R. Gupta, D. Srivastava, M. Sahu, et al. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Molecular Diversity, 2021, 25: 1315−1360. https://doi.org/10.1007/s11030-021-10217-3
D. Paul, G. Sanap, S. Shenoy, et al. Artificial intelligence in drug discovery and development. Drug Discovery Today, 2021, 26(1): 80−93. https://doi.org/10.1016/j.drudis.2020.10.010
G. S. Liang, W. G. Fan, H. Luo, et al. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine &Pharmacotherapy, 2020, 128: 110255. https://doi.org/10.1016/j.biopha.2020.110255
K.-K. Mak, M.R. Pichika. Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 2019, 24(3): 773−780. https://doi.org/10.1016/j.drudis.2018.11.014
B.Y. Feng, A. Simeonov, A. Jadhav, et al. A high-throughput screen for aggregation-based inhibition in a large compound library. Journal of Medicinal Chemistry, 2007, 50(10): 2385−2390. https://doi.org/10.1021/jm061317y
C. Selvaraj, I. Chandra, S.K. Singh. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries. Molecular Diversity, 2022, 26(3): 1893−1913. https://doi.org/10.1007/s11030-021-10326-z
N. Stephenson, E. Shane, J. Chase, et al. Survey of machine learning techniques in drug discovery. Current Drug Metabolism, 2019, 20(3): 185−193. https://doi.org/10.2174/1389200219666180820112457
O. Khan, M. Parvez, P. Kumari, et al. The future of pharmacy: How AI is revolutionizing the industry. Intelligent Pharmacy, 2023, 1(1): 32−40. https://doi.org/10.1016/j.ipha.2023.04.008
P. Stone, M. Veloso. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 2000, 8: 345−383. https://doi.org/10.1023/A:1008942012299
I. Donmez, S. Idin, S. Gulen. Conducting academic research with the AI interface ChatGPT: Challenges and opportunities. Journal of Steam Education, 2023, 6(2): 101−118.
J.L. Ruiz-Real, J. Uribe-Toril, J.A. Torres, et al. Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 2020, 22(1): 98−117. https://doi.org/10.3846/jbem.2020.13641
M. Bhat, M. Rabindranath. The promise of artificial intelligence for predictive biomarkers in hepatology. Hepatology International, 2022, 16(3): 523−525. https://doi.org/10.1007/s12072-022-10342-7
Z.Y. Low, I.A. Farouk, S.K. Lal. Drug repositioning: New approaches and future prospects for life-debilitating diseases and the COVID-19 pandemic outbreak. Viruses, 2020, 12(9): 1058. https://doi.org/10.3390/v12091058
X.Y. Zhu, Y. Li, N. Gu. Application of artificial intelligence in the exploration and optimization of biomedical nanomaterials. Nano Biomedicine and Engineering, 2023, 15: 342−353. https://doi.org/10.26599/NBE.2023.9290035
A.V. Sadybekov, V. Katritch. Computational approaches streamlining drug discovery. Nature, 2023, 616(7958): 673−685. https://doi.org/10.1038/s41586-023-05905-z
M. Koromina, M.T. Pandi, G.P. Patrinos. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. OMICS:A Journal of Integrative Biology, 2019, 23(11): 539−548. https://doi.org/10.1089/omi.2019.0151
J.M. Levin, T.I. Oprea, S. Davidovich, et al. Artificial intelligence, drug repurposing and peer review. Nature Biotechnology, 2020, 38(10): 1127−1131. https://doi.org/10.1038/s41587-020-0686-x
L.M. Williams, A.J. Rush, S.H. Koslow, et al. International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: Rationale and protocol. Trials, 2011, 12: 4. https://doi.org/10.1186/1745-6215-12-4
S.K. Bhattamisra, P. Banerjee, P. Gupta, et al. Artificial intelligence in pharmaceutical and healthcare research. Big Data and Cognitive Computing, 2023, 7(1): 10. https://doi.org/10.3390/bdcc7010010
S. Graham, C. Depp, E.E. Lee, et al. Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 2019, 21(11): 116. https://doi.org/10.1007/s11920-019-1094-0
W.J. Yan, Q.N. Ruan, K. Jiang. Challenges for artificial intelligence in recognizing mental disorders. Diagnostics, 2022, 13(1): 2. https://doi.org/10.3390/diagnostics13010002
B. Bhinder, C. Gilvary, N.S. Madhukar, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discovery, 2021, 11(4): 900−915. https://doi.org/10.1158/2159-8290.CD-21-0090
R. Hamamoto, K. Suvarna, M. Yamada, et al. Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers, 2020, 12(12): 3532. https://doi.org/10.3390/cancers12123532
L. Surya. How government can use AI and ML to identify spreading infectious diseases. International Journal of Creative Research Thoughts, 2018, 6(1): 2320−2882.
G. Russo, P. Reche, M. Pennisi, et al. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opinion on Drug Discovery, 2020, 15(11): 1267−1281. https://doi.org/10.1080/17460441.2020.1791076
J.L. Excler, M. Saville, S. Berkley, et al. Vaccine development for emerging infectious diseases. Nature Medicine, 2021, 27(4): 591−600. https://doi.org/10.1038/s41591-021-01301-0
A.S. Ahuja, V.P. Reddy, O. Marques. Artificial intelligence and COVID-19: A multidisciplinary approach. Integrative Medicine Research, 2020, 9(3): 100434. https://doi.org/10.1016%2Fj.imr.2020.100434
G. Arora, J. Joshi, R.S. Mandal, et al. Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development against COVID-19. Pathogens, 2021, 10(8): 1048. https://doi.org/10.3390/pathogens10081048
A. Hosny, C. Parmar, J. Quackenbush, et al. Artificial intelligence in radiology. Nature Reviews Cancer, 2018, 18(8): 500−510. https://doi.org/10.1038/s41568-018-0016-5
J.H. Thrall, X. Li, Q.Z. Li, et al. Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 2018, 15(3): 504−508. https://doi.org/10.1016/j.jacr.2017.12.026
M. Reyes, R. Meier, S. Pereira, et al. On the interpretability of artificial intelligence in radiology: Challenges and opportunities. Radiology:Artificial Intelligence, 2020, 2(3): e190043. https://doi.org/10.1148/ryai.2020190043
S. Saxena, B. Jena, N. Gupta, et al. Role of artificial intelligence in radiogenomics for cancers in the era of precision medicine. Cancers, 2022, 14(12): 2860. https://doi.org/10.3390/cancers14122860
J.W. Lao, Y.S. Chen, Z.C. Li, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Scientific Reports, 2017, 7: 10353. https://doi.org/10.1038/s41598-017-10649-8
I. Kulkov. The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technology in Society, 2021, 66: 101629. https://doi.org/10.1016/j.techsoc.2021.101629
J. Sidlauskiene, Y. Joye, V. Auruskeviciene. AI-based chatbots in conversational commerce and their effects on product and price perceptions. Electronic Markets, 2023, 33: 24. https://doi.org/10.1007/s12525-023-00633-8
D. Khurana, A. Koli, K. Khatter, et al. Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 2023, 82(3): 3713−3744. https://doi.org/10.1007/s11042-022-13428-4
L. Jenneboer, C. Herrando, E. Constantinides. The impact of chatbots on customer loyalty: A systematic literature review. Journal of Theoretical and Applied Electronic Commerce Research, 2022, 17(1): 212−229. https://doi.org/10.3390/jtaer17010011
M. Hasal, J. Nowaková, K. Ahmed Saghair, et al. Chatbots: Security, privacy, data protection, and social aspects. Concurrency and Computation:Practice and Experience, 2021, 33(19): e6426. https://doi.org/10.1002/cpe.6426
Y.N. Harari. Reboot for the AI revolution. Nature, 2017, 550(7676): 324−327. https://doi.org/10.1038/550324a
S. Livingston, M. Risse. The future impact of artificial intelligence on humans and human rights. Ethics &International Affairs, 2019, 33(2): 141−158. https://doi.org/10.1017/S089267941900011X
Y. K. Dwivedi, L. Hughes, E. Ismagilova, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 2021, 57: 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
B. Meskó, E.J. Topol. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. Npj Digital Medicine, 2023, 6: 120. https://doi.org/10.1038/s41746-023-00873-0
M. Braun, P. Hummel, S. Beck, et al. Primer on an ethics of AI-based decision support systems in the clinic. Journal of Medical Ethics, 2021, 47(12): e3. https://doi.org/10.1136/medethics-2019-105860
P. Schmidt, F. Biessmann, T. Teubner. Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 2020, 29(4): 260−278. https://doi.org/10.1080/12460125.2020.1819094
N. Naik, B.M.Z. Hameed, D.K. Shetty, et al. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility. Frontiers in Surgery, 2022, 9: 862322. https://doi.org/10.3389/fsurg.2022.86232
C. González-Gonzalo, E.F. Thee, C.C.W. Klaver, et al. Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice. Progress in Retinal and Eye Research, 2022, 90: 101034. https://doi.org/10.1016/j.preteyeres.2021.101034
M. Ayaz, M.F. Pasha, M.Y. Alzahrani, et al. The fast health interoperability resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 2021, 9(8): e32869. https://doi.org/10.2196/21929
B.H.M. van der Velden, H.J. Kuijf, K.G.A. Gilhuijs, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 2022, 79: 102470. https://doi.org/10.1016/j.media.2022.102470
E.V. Bernstam, P.K. Shireman, F. Meric-Bernstam, et al. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities. Clinical and Translational Science, 2022, 15(2): 309−321. https://doi.org/10.1111/cts.13175