Home The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease
Article
Licensed
Unlicensed Requires Authentication

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease

  • Mohammad Abu Zahra , Abdulla Al-Taher , Mohamed Alquhaidan , Tarique Hussain , Izzeldin Ismail , Indah Raya and Mahmoud Kandeel EMAIL logo
Published/Copyright: July 15, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Drug Metabolism and Personalized Therapy
From the journal Drug Metabolism and Personalized Therapy Volume 39 Issue 2

Abstract

Introduction

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments.

Content

Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management.

Summary

The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries.

Outlook

As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Keywords: personalized medicine; drug response; treatment efficiency; genetic variations; pharmacogenomics
Corresponding author: Mahmoud Kandeel, Department of Biomedical Sciences, College of Veterinary Medicine, King Faisal University, Al-Hofuf, 31982, Al-Ahsa, Saudi Arabia; and Department of Pharmacology, Faculty of Veterinary Medicine, Kafrelshikh University, Kafrelshikh, Egypt, E-mail:

Funding source: The Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia

Award Identifier / Grant number: GRANTA274

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: This work was supported by the Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANTA274].

  6. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Freimer, N, Sabatti, C. The human phenome project. Nat Genet 2003;34:15–21. https://doi.org/10.1038/ng0503-15.Search in Google Scholar PubMed

2. Brittain, HK, Scott, R, Thomas, E. The rise of the genome and personalised medicine. Clin Med 2017;17:545–51. https://doi.org/10.7861/clinmedicine.17-6-545.Search in Google Scholar PubMed PubMed Central

3. Harvey, A, Brand, A, Holgate, ST, Kristiansen, LV, Lehrach, H, Palotie, A, et al.. The future of technologies for personalised medicine. N Biotech 2012;29:625–33. https://doi.org/10.1016/j.nbt.2012.03.009.Search in Google Scholar PubMed

4. Horgan, D. From here to 2025: personalised medicine and healthcare for an immediate future. J Cancer Policy 2018;16:6–21. https://doi.org/10.1016/j.jcpo.2017.12.008.Search in Google Scholar

5. Kandeel, M. Oncogenic viruses-encoded microRNAs and their role in the progression of cancer: emerging targets for antiviral and anticancer therapies. Pharmaceuticals 2023;16:485. https://doi.org/10.3390/ph16040485.Search in Google Scholar PubMed PubMed Central

6. Kandeel, M, Althumairy, D, El-Sabagh, IM, Shousha, S, Hussin, Y, Meligy, AM, et al.. The interaction of programmed cell death protein and its ligands with non-coding RNAs in neoplasms: emerging anticancer immunotherapeutics. Processes 2023;11:538. https://doi.org/10.3390/pr11020538.Search in Google Scholar

7. Kandeel, M, Morsy, MA, El-Lateef, HMA, Marzok, M, El-Beltagi, HS, Venugopala, VN. Upregulation of microRNA-146a increases the sensitivity of cisplatin-resistant lung cancer cells to chemotherapy through the toll-like receptor-signaling pathway. Int J Pharmacol 2023;19:749–57. https://doi.org/10.3923/ijp.2023.749.757.Search in Google Scholar

8. Chen, X, Mangala, LS, Rodriguez-Aguayo, C, Kong, X, Lopez-Berestein, G, Sood, AK. RNA interference-based therapy and its delivery systems. Cancer Metastasis Rev 2018;37:107–24. https://doi.org/10.1007/s10555-017-9717-6.Search in Google Scholar PubMed PubMed Central

9. Mansoori, B, Sandoghchian Shotorbani, S, Baradaran, B. RNA interference and its role in cancer therapy. Adv Pharmaceut Bull 2014;4:313–21. https://doi.org/10.5681/apb.2014.046.Search in Google Scholar PubMed PubMed Central

10. Damase, TR, Sookhovershin, R, Boada, C, Pettigrew, RI, Cooke, JP. The limitless future of RNA therapeutics. Front Bioeng Biotechnol 2021;9:628137. https://doi.org/10.3389/fbioe.2021.628137.Search in Google Scholar PubMed PubMed Central

11. Agrawal, S, Heiss, MS, Fenter, RB, Abramova, TV, Perera, MA, Pacheco, JA, et al.. Impact of CYP2C9-interacting drugs on warfarin pharmacogenomics. Clin Transl Sci 2020;13:941–9. https://doi.org/10.1111/cts.12781.Search in Google Scholar PubMed PubMed Central

12. Anguela, XM, High, KA. Entering the modern era of gene therapy. Annu Rev Med 2019;70:273–88. https://doi.org/10.1146/annurev-med-012017-043332.Search in Google Scholar PubMed

13. Birhan, TA, Molla, MD, Abdulkadir, M, Tesfa, KH. Association of angiotensin-converting enzyme gene insertion/deletion polymorphisms with risk of hypertension among the Ethiopian population. PLoS One 2022;17:e0276021. https://doi.org/10.1371/journal.pone.0276021.Search in Google Scholar PubMed PubMed Central

14. Canaud, B, Stuard, S, Laukhuf, F, Yan, G, Canabal, MIG, Lim, PS, et al.. Choices in hemodialysis therapies: variants, personalized therapy and application of evidence-based medicine. Clin Kidney J 2021;14:i45–8. https://doi.org/10.1093/ckj/sfab198.Search in Google Scholar PubMed PubMed Central

15. Cheng, J, Ha, M, Wang, Y, Sun, J, Chen, J, Wang, Y, et al.. A C118T polymorphism of ERCC1 and response to cisplatin chemotherapy in patients with late-stage non-small cell lung cancer. J Cancer Res Clin Oncol 2012;138:231–8. https://doi.org/10.1007/s00432-011-1090-1.Search in Google Scholar PubMed

16. Collins, KS, Raviele, ALJ, Elchynski, AL, Woodcock, AM, Zhao, Y, Cooper-DeHoff, RM, et al.. Genotype-guided hydralazine therapy. Am J Nephrol 2020;51:764–76. https://doi.org/10.1159/000510433.Search in Google Scholar PubMed PubMed Central

17. Duong, JK, Nand, RA, Patel, A, Della Pasqua, O, Gross, AS. A physiologically based pharmacokinetic model of clopidogrel in populations of European and Japanese ancestry: an evaluation of CYP2C19 activity. Pharmacol Res Perspect 2022;10:e00946. https://doi.org/10.1002/prp2.946.Search in Google Scholar PubMed PubMed Central

18. Halushka, MK, Walker, LP, Halushka, PV. Genetic variation in cyclooxygenase 1: effects on response to aspirin. Clin Pharmacol Ther 2003;73:122–30. https://doi.org/10.1067/mcp.2003.1.Search in Google Scholar PubMed

19. Niespodziana, K, Borochova, K, Pazderova, P, Schlederer, T, Astafyeva, N, Baranovskaya, T, et al.. Toward personalization of asthma treatment according to trigger factors. J Allergy Clin Immunol 2020;145:1529–34. https://doi.org/10.1016/j.jaci.2020.02.001.Search in Google Scholar PubMed PubMed Central

20. Ordovas, JM, Ferguson, LR, Shyong Tai, E, Mathers, JC. Personalised nutrition and health. BMJ 2018;361:k2173. https://doi.org/10.1136/bmj.k2173.Search in Google Scholar PubMed PubMed Central

21. Simper, GS, Hò, GT, Celik, AA, Huyton, T, Kuhn, J, Kunze-Schumacher, H, et al.. Carbamazepine-mediated adverse drug reactions: CBZ-10,11-epoxide but not carbamazepine induces the alteration of peptides presented by HLA-B *15:02. J Immunol Res 2018;2018:5086503–12. https://doi.org/10.1155/2018/5086503.Search in Google Scholar PubMed PubMed Central

22. Thomas, L, Raju, AP, Chaithra, MSS, Varma, M, Saravu, K, Banerjee, M, et al.. Influence of N-acetyltransferase 2 (NAT2) genotype/single nucleotide polymorphisms on clearance of isoniazid in tuberculosis patients: a systematic review of population pharmacokinetic models. Eur J Clin Pharmacol 2022;78:1535–53. https://doi.org/10.1007/s00228-022-03362-7.Search in Google Scholar PubMed PubMed Central

23. Yue, YH, Bai, XD, Zhang, HJ, Li, YM, Hu, L, Liu, LY, et al.. Gene polymorphisms affect the effectiveness of atorvastatin in treating ischemic stroke patients. Cell Physiol Biochem 2016;39:630–8. https://doi.org/10.1159/000445654.Search in Google Scholar PubMed

24. Cooper-DeHoff, RM, Niemi, M, Ramsey, LB, Luzum, JA, Tarkiainen, EK, Straka, RJ, et al.. The clinical pharmacogenetics implementation consortium guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and statin-associated musculoskeletal symptoms. Clin Pharmacol Ther 2022;111:1007–21. https://doi.org/10.1002/cpt.2557.Search in Google Scholar PubMed PubMed Central

25. Dean, L. Carbamazepine therapy and HLA genotype. In: Pratt, VM, Scott, SA, Pirmohamed, M, Esquivel, B, Kattman, BL, Malheiro, AJ, editors. Medical genetics summaries. Bethesda, MD: National Center for Biotechnology Information; 2012. Available from: https://www.ncbi.nlm.nih.gov/books/NBK321445/.Search in Google Scholar

26. Castro-Barquero, S, Scott, SA, Pirmohamed, M, Esquivel, B, Kattman, BL, Malheiro, AJ. Dietary strategies for metabolic syndrome: a comprehensive review. Nutrients 2020;12:2983. https://doi.org/10.3390/nu12102983.Search in Google Scholar PubMed PubMed Central

27. Cao, L, Zhang, Z, Sun, W, Bai, W, Sun, W, Zhang, Y, et al.. Impacts of COX-1 gene polymorphisms on vascular outcomes in patients with ischemic stroke and treated with aspirin. Gene 2014;546:172–6. https://doi.org/10.1016/j.gene.2014.06.023.Search in Google Scholar PubMed

28. Chang, KL, Weitzel, K, Schmidt, S. Pharmacogenetics: using genetic information to guide drug therapy. Am Fam Physician 2015;92:588–94.Search in Google Scholar

29. Abilify (aripiprazole) package insert. Princeton, NJ: Bristol-Myers Squibb. 2011.Search in Google Scholar

30. Collett, S, Massmann, A, Petry, NJ, Van Heukelom, J, Schultz, A, Hellwig, T, et al.. Metoprolol and CYP2D6: a retrospective cohort study evaluating genotype-based outcomes. J Personalized Med 2023;13:416. https://doi.org/10.3390/jpm13030416.Search in Google Scholar PubMed PubMed Central

31. Alsaleem, A, Shousha, S, Marzok, M, Afzal, S, Alameen, A, Albokhadaim, I, et al.. Transformative technologies for the future of camel welfare: artificial intelligence for improved diagnostics, therapeutics and health outcomes. J Camel Pract Res 2024;31:1–9. https://doi.org/10.5958/2277-8934.2024.00001.8.Search in Google Scholar

32. Kandeel, M. Revolutionizing healthcare: harnessing the power of artificial intelligence for enhanced diagnostics, treatment and drug discovery. Int J Pharmacol 2024;20:1–10. https://doi.org/10.3923/ijp.2024.1.10.Search in Google Scholar

33. Kandeel, M, Al-Mubarak, AIA, Alhojaily, S, Afzal, S, Ismail, I, Al-Rasheed, M. Revolutionizing antiviral drug discovery: the emerging role of artificial intelligence. Int J Pharmacol 2024;20:536–46. https://doi.org/10.3923/ijp.2024.536.546.Search in Google Scholar

34. Lin, TC. Artificial intelligence, finance, and the law. Fordham Law Rev 2019;88:531.Search in Google Scholar

35. Abduljabbar, R, Dia, H, Liyanage, S, Bagloee, SA. Applications of artificial intelligence in transport: an overview. Sustainability 2019;11:189. https://doi.org/10.3390/su11010189.Search in Google Scholar

36. Alto, V. Modern generative AI with ChatGPT and OpenAI models: leverage the capabilities of OpenAI’s LLM for productivity and innovation with GPT3 and GPT4. Packt Publishing Ltd; 2023.Search in Google Scholar

37. Soydaner, D. Attention mechanism in neural networks: where it comes and where it goes. Neural Comput Appl 2022;34:13371–85. https://doi.org/10.1007/s00521-022-07366-3.Search in Google Scholar

38. Liu, Y, Han, T, Ma, S, Zhang, J, Yang, Y, Tian, J, et al.. Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology 2023;1:100017. https://doi.org/10.1016/j.metrad.2023.100017.Search in Google Scholar

39. Gorenstein, L, Konen, E, Green, M, Klang, E. Bidirectional encoder representations from transformers in radiology: a systematic review of natural Language Processing applications. J Am Coll Radiol 2024;21:914–41. https://doi.org/10.1016/j.jacr.2024.01.012.Search in Google Scholar PubMed

40. Patel, R, Patel, S. A comprehensive study of applying convolutional neural network for computer vision. Int J Adv Sci Technol 2020;6:2161–74.Search in Google Scholar

41. Bohr, A, Memarzadeh, K. The rise of artificial intelligence in healthcare applications. In: Artificial intelligence in healthcare. Elsevier; 2020:25–60 pp.10.1016/B978-0-12-818438-7.00002-2Search in Google Scholar

42. Li, SE. Introduction to reinforcement learning. In: Li, SE, editor. Reinforcement learning for sequential decision and optimal control. Singapore: Springer Nature Singapore; 2023:pp. 1–14 pp.10.1007/978-981-19-7784-8_1Search in Google Scholar

43. Kaur, D, Uslu, S, Rittichier, KJ, Durresi, A. Trustworthy artificial intelligence: a review. ACM Comput Surv 2022;55:1–38. https://doi.org/10.1145/3491209.Search in Google Scholar

44. Bellamy, RK, Dey, K, Hind, M, Hoffman, SC, Houde, S, Kannan, K, et al.. AI Fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J Res Dev 2019;63:15–4. https://doi.org/10.1147/jrd.2019.2942287.Search in Google Scholar

45. Páez, A. The pragmatic turn in explainable artificial intelligence (XAI). Minds Mach 2019;29:441–59. https://doi.org/10.1007/s11023-019-09502-w.Search in Google Scholar

46. Dou, B, Zhu, Z, Merkurjev, E, Ke, L, Chen, L, Jiang, J, et al.. Machine learning methods for small data challenges in molecular science. Chem Rev 2023;123:8736–80. https://doi.org/10.1021/acs.chemrev.3c00189.Search in Google Scholar PubMed PubMed Central

47. Dias, R, Torkamani, A. Artificial intelligence in clinical and genomic diagnostics. Genome Med 2019;11:70. https://doi.org/10.1186/s13073-019-0689-8.Search in Google Scholar PubMed PubMed Central

48. Alvarez-Machancoses, O, DeAndrés Galiana, EJ, Cernea, A, Fernández de la Viña, J, Fernández-Martínez, JL. On the role of artificial intelligence in genomics to enhance precision medicine. Pharmgenomics Pers Med 2020;13:105–19. https://doi.org/10.2147/pgpm.s205082.Search in Google Scholar PubMed PubMed Central

49. Fishman, V, Sindeeva, M, Chekanov, N, Shashkova, T, Ivanisenko, N, Kardymon, O. AI in genomics and epigenomics. In: Moskalev, A, Stambler, I, Zhavoronkov, A, editors. Artificial intelligence for healthy longevity. Springer; 2023.10.1007/978-3-031-35176-1_11Search in Google Scholar

50. 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 2021;16:991–1007. https://doi.org/10.1080/17460441.2021.1918096.Search in Google Scholar PubMed

51. Malandraki-Miller, S, Riley, PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021;26:887–901. https://doi.org/10.1016/j.drudis.2021.01.013.Search in Google Scholar PubMed

52. Álvarez-Machancoses, Ó, Fernández-Martínez, JL. Using artificial intelligence methods to speed up drug discovery. Expet Opin Drug Discov 2019;14:769–77. https://doi.org/10.1080/17460441.2019.1621284.Search in Google Scholar PubMed

53. Speck-Planche, A, Cordeiro, MN. Enabling virtual screening of potent and safer antimicrobial agents against noma: mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties. Mini Rev Med Chem 2015;15:194–202. https://doi.org/10.2174/138955751503150312120519.Search in Google Scholar PubMed

54. Tenorio-Borroto, E, Ramirez, FR, Speck-Planche, A, Cordeiro, MN, Luan, F, Gonzalez-Diaz, H. QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Curr Drug Metabol 2014;15:414–28. https://doi.org/10.2174/1389200215666140908101152.Search in Google Scholar PubMed

55. Wang, P. On defining artificial intelligence. J Artif Gen Intell 2019;10:1–37.10.2478/jagi-2019-0002Search in Google Scholar

56. Chen, PC, Gadepalli, K, MacDonald, R, Liu, Y, Kadowaki, S, Nagpal, K, et al.. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med 2019;25:1453–7. https://doi.org/10.1038/s41591-019-0539-7.Search in Google Scholar PubMed

57. Bergl, PA, Zhou, Y. Diagnostic error in the critically ill: a hidden epidemic? Crit Care Clin 2022;38:11–25. https://doi.org/10.1016/j.ccc.2021.09.005.Search in Google Scholar PubMed

58. Newman-Toker, DE, Schaffer, AC, Yu-Moe, CW, Nassery, N, Saber Tehrani, AS, Clemens, GD, et al.. Serious misdiagnosis-related harms in malpractice claims: the “Big Three”―vascular events, infections, and cancers. Diagnosis 2019;6:227–40. https://doi.org/10.1515/dx-2019-0019.Search in Google Scholar PubMed

59. Kaur, S, Singla, J, Nkenyereye, L, Jha, S, Prashar, D, Prasad Joshi, GP, et al.. Medical diagnostic systems using artificial intelligence (AI) algorithms: principles and perspectives. IEEE Access 2020;8:228049–69. https://doi.org/10.1109/access.2020.3042273.Search in Google Scholar

60. Issaiy, M, Zarei, D, Saghazadeh, A. Artificial intelligence and acute appendicitis: a systematic review of diagnostic and prognostic models. World J Emerg Surg 2023;18:59. https://doi.org/10.1186/s13017-023-00527-2.Search in Google Scholar PubMed PubMed Central

61. Farabi, MS, Yousefi, M, Afshar, S, Pedrammehr, S, Lim, CP, Jafarizadeh, A, et al.. Artificial intelligence for multiple sclerosis management using retinal images: pearl, peaks, and pitfalls. Semin Ophthalmol 2024;39:271–88. https://doi.org/10.1080/08820538.2023.2293030. 38088176.Search in Google Scholar PubMed

62. Zahra, MA, Kandeel, M, Aldossary, SA, Al-Taher, A. Study on genotyping polymorphism and sequencing of N-acetyltransferase 2 (NAT2) among Al-Ahsa population. BioMed Res Int 2020;2020:8765347–9. https://doi.org/10.1155/2020/8765347.Search in Google Scholar PubMed PubMed Central

63. Jianzhu, B, Shuang, L, Pengfai, M, Yi, Z, Yanshu, Z. Research on early warning mechanism and model of liver cancer rehabilitation based on CS-SVM. J Healthc Eng 2021;2021:6658776. https://doi.org/10.1155/2021/6658776.Search in Google Scholar PubMed PubMed Central

64. Liu, C, Jiao, D, Liu, Z. Artificial intelligence (AI)-aided disease prediction. BIO Integration 2020;1:130–6. https://doi.org/10.15212/bioi-2020-0017.Search in Google Scholar

65. Rajpurkar, P, Chen, E, Banerjee, O, Topol, EJ. AI in health and medicine. Nat Med 2022;28:31–8. https://doi.org/10.1038/s41591-021-01614-0.Search in Google Scholar PubMed

66. Santos, MK, Ferreira, JRJr, Wada, DT, Magalhaes Tonorio, AP, Nogueira-Barbosa, MH, de Azeveda Marques, PM. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards precision medicine. Radiol Bras 2019;52:387–96. https://doi.org/10.1590/0100-3984.2019.0049.Search in Google Scholar PubMed PubMed Central

67. Torrente, M, Sousa, PA, Hernández, R, Blanco, M, Calvo, V, Collazo, A, et al.. An artificial intelligence-based tool for data analysis and prognosis in cancer patients: results from the Clarify Study. Cancers 2022;14:4041. https://doi.org/10.3390/cancers14164041.Search in Google Scholar PubMed PubMed Central

68. van de Sande, D, Sharabiani, M, Bluemink, H, Kneepkens, E, Bakx, N, Hagelaar, E, et al.. Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer. Phys Imaging Radiat Oncol 2021;20:111–6. https://doi.org/10.1016/j.phro.2021.11.007.Search in Google Scholar PubMed PubMed Central

69. Shan, G, Li, X, Huang, W. AI-enabled wearable and flexible electronics for assessing full personal exposures. Innovation 2020;1:100031. https://doi.org/10.1016/j.xinn.2020.100031.Search in Google Scholar PubMed PubMed Central

70. Jiang, Y, Yang, Y, Shen, L, Ma, J, Ma, H, Zhu, N. Recent advances of Prussian blue-based wearable biosensors for healthcare. Anal Chem 2022;94:297–311. https://doi.org/10.1021/acs.analchem.1c04420.Search in Google Scholar PubMed

71. Sabry, F, Eltaras, T, Labda, W, Alzoubi, K, Malluhi, Q. Machine learning for healthcare wearable devices: the big picture. J Healthc Eng 2022;2022:4653923–5. https://doi.org/10.1155/2022/4653923.Search in Google Scholar PubMed PubMed Central

72. Iqbal, SMA, Mahgoub, I, Du, E, Leavitt, MA, Asghar, W. Advances in healthcare wearable devices. NPJ Flex Electron 2021;5:1–14. https://doi.org/10.1038/s41528-021-00107-x.Search in Google Scholar

73. Johnson, KB, Wei, WQ, Weeraratne, D, Frisse, ME, Misulis, K, Rhee, K, et al.. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021;14:86–93. https://doi.org/10.1111/cts.12884.Search in Google Scholar PubMed PubMed Central

74. Geneviève, LD, Martani, A, Shaw, D, Elger, BS, Wangmo, T. Structural racism in precision medicine: leaving no one behind. BMC Med Ethics 2020;21:1–13. https://doi.org/10.1186/s12910-020-0457-8.Search in Google Scholar PubMed PubMed Central

75. Murdoch, B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 2021;22:122. https://doi.org/10.1186/s12910-021-00687-3.Search in Google Scholar PubMed PubMed Central

76. Seh, AH, Zarour, M, Alenezi, M, Sarkar, AK, Agrawal, A, Kumar, R, et al.. Healthcare data breaches: insights and implications. Healthcare 2020;13:133. https://doi.org/10.3390/healthcare8020133.Search in Google Scholar PubMed PubMed Central

77. Mills, S, Costa, S, Sunstein, CR. The opportunities and costs of AI in behavioural science; 2023. Available at SSRN: 4490597.10.2139/ssrn.4490597Search in Google Scholar

78. Naithani, N, Atal, AT, Tilak, TV, Vasudevan, B, Misra, P, Sinha, S. Precision medicine: uses and challenges. Med J Armed Forces India 2021;77:258–65. https://doi.org/10.1016/j.mjafi.2021.06.020.Search in Google Scholar PubMed PubMed Central

79. Elbadawi, M, McCoubrey, LE, Gavins, FKH, Ong, JJ, Goyanes, A, Gaisford, S, et al.. Harnessing artificial intelligence for the next generation of 3D printed medicines. Adv Drug Deliv Rev 2021;175:113805. https://doi.org/10.1016/j.addr.2021.05.015.Search in Google Scholar PubMed

80. Mahomed, S, Padayatchi, N, Singh, J, Naidoo, K. Precision medicine in resistant tuberculosis: treat the correct patient, at the correct time, with the correct drug. J Infect 2019;78:261–8. https://doi.org/10.1016/j.jinf.2019.03.006.Search in Google Scholar PubMed

81. Bezbaruah, R, Ghosh, ML, Kumari, S, Nongrang, L, Ali, SR, Lahiri, M, et al.. In: Chavda, V, Anand, K, Apostolopoulos, V, editors. Bioinformatics tools for pharmaceutical drug product development. Scrivenor; 2023:pp. 345–69 pp.10.1002/9781119865728.ch15Search in Google Scholar

82. Marcus, JL, Sewell, WC, Balzer, LB, Krakower, DS. Artificial intelligence and machine learning for HIV prevention: emerging approaches to ending the epidemic. Curr HIV AIDS Rep 2020;17:171–9. https://doi.org/10.1007/s11904-020-00490-6.Search in Google Scholar PubMed PubMed Central

Received: 2024-01-10
Accepted: 2024-06-17
Published Online: 2024-07-15

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease
  4. Original Articles
  5. Helicobacter pylori eradication therapy for children
  6. Clinical and pharmacogenetic features of patients with upper gastrointestinal lesions at a multidisciplinary hospital: the role of nonsteroidal anti-inflammatory drugs
  7. Effects of tumor necrosis factor-α rs1800629 and interleukin-10 rs1800872 genetic variants on type 2 diabetes mellitus susceptibility and metabolic parameters among Jordanians
  8. The impact of ABCB1, CYP3A4 and CYP3A5 gene polymorphisms on apixaban trough concentration and bleeding risk in patients with atrial fibrillation
  9. Case Report
  10. CYP2D6 inhibition by diphenhydramine leading to fatal hydrocodone overdose
https://doi.org/10.1515/dmpt-2024-0003
Keywords for this article
personalized medicine; drug response; treatment efficiency; genetic variations; pharmacogenomics

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease
  4. Original Articles
  5. Helicobacter pylori eradication therapy for children
  6. Clinical and pharmacogenetic features of patients with upper gastrointestinal lesions at a multidisciplinary hospital: the role of nonsteroidal anti-inflammatory drugs
  7. Effects of tumor necrosis factor-α rs1800629 and interleukin-10 rs1800872 genetic variants on type 2 diabetes mellitus susceptibility and metabolic parameters among Jordanians
  8. The impact of ABCB1, CYP3A4 and CYP3A5 gene polymorphisms on apixaban trough concentration and bleeding risk in patients with atrial fibrillation
  9. Case Report
  10. CYP2D6 inhibition by diphenhydramine leading to fatal hydrocodone overdose
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 2.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/dmpt-2024-0003/html?lang=en&srsltid=AfmBOooxR0YrLIf-ZCXSBR04IsM60hBqlG8_JVQIYiLjKMTE1XCm_fsj
Scroll to top button