Skip to content
2000
image of Cloud Computing Facilitating Data Storage, Collaboration, and Analysis in Global Healthcare Clinical Trials

Abstract

Introduction

Healthcare data management, especially in the context of clinical trials, has been completely transformed by cloud computing. It makes it easier to store data, collaborate in real time, and perform advanced analytics across international research networks by providing scalable, secure, and affordable solutions. This paper explores how cloud computing is revolutionizing clinical trials, tackling issues including data integration, accessibility, and regulatory compliance.

Materials and Methods

Key factors assessed include cloud platform-enabled analytical tools, collaborative features, and data storage capacity. To ensure the safe management of sensitive healthcare data, adherence to laws like GDPR and HIPAA was emphasized.

Results

Real-time updates and integration of multicenter trial data were made possible by cloud systems, which also showed notable gains in collaborative workflows and data sharing. High scalability storage options reduced infrastructure expenses while upholding security requirements. Rapid interpretation of complicated datasets was made possible by sophisticated analytical tools driven by machine learning and artificial intelligence, which expedited decision-making. Improved patient recruitment tactics and flexible trial designs are noteworthy examples.

Conclusion

Cloud computing has become essential for international clinical trials because it provides unmatched efficiency in data analysis, communication, and storage. It is a pillar of contemporary healthcare research due to its capacity to guarantee data security and regulatory compliance as well as its creative analytical capabilities. Subsequent research ought to concentrate on further refining cloud solutions to tackle new issues and utilizing their complete capabilities in clinical trial administration.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/rrct/10.2174/0115748871379249250701065507
2025-07-17
2025-10-10

  • From This Site

    /content/journals/rrct/10.2174/0115748871379249250701065507
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Hussein R. Wurhofer D. Strumegger E.M. General data protection regulation (GDPR) toolkit for digital health. Stud. Health Technol. Inform. 2022 290 222 226 10.3233/SHTI220066 35673005
    [Google Scholar]
  2. Mondschein C.F. Monda C. Chapter 5 the eu’s general data protection regulation (GDPR) in a research context. In: Kubben P, Dumontier M, Dekker A, Eds. Fundamentals of Clinical Data Science. Cham, CH: Springer 2019
    [Google Scholar]
  3. Carmi L. Zohar M. Riva G.M. The european general data protection regulation (GDPR) in mhealth: Theoretical and practical aspects for practitioners’. Med. Sci. Law 2023 63 1 61 68 10.1177/00258024221118411 35950240
    [Google Scholar]
  4. Fatehi Farhad Hassandoust Farkhondeh Akhlaghpour Saeed Akhlaghpour S. General data protection regulation (GDPR) in healthcare: Hot topics and research fronts. Stud. Health Technol. Inform. 2020 270 1118 1122 10.3233/SHTI200336 32570555
    [Google Scholar]
  5. Cornock M. General data protection regulation (GDPR) and implications for research. Maturitas 2018 111 A1 A2 10.1016/j.maturitas.2018.01.017 29395455
    [Google Scholar]
  6. Powell S.K. HIPAA. Lippincotts Case Manag. 2003 8 1 1 2 10.1097/00129234‑200301000‑00001 12555037
    [Google Scholar]
  7. Gayler B.W. HIPAA regulations. J. Am. Coll. Radiol. 2005 2 2 200 201 10.1016/j.jacr.2004.12.003 17411795
    [Google Scholar]
  8. Schoppmann M.J. Sanders D.L. HIPAA compliance: The law, reality, and recommendations. J. Am. Coll. Radiol. 2004 1 10 728 733 10.1016/j.jacr.2004.03.017 17411692
    [Google Scholar]
  9. Boyce B. Emerging technology and the health insurance portability and accountability act. J. Acad. Nutr. Diet. 2017 117 4 517 518 10.1016/j.jand.2016.05.013 27436531
    [Google Scholar]
  10. Prince L.H. Carroll-Barefield A. Management implications of the health insurance portability and accountability act. Health Care Manag. 2000 19 1 44 49 10.1097/00126450‑200019010‑00008 11183652
    [Google Scholar]
  11. Hucíková A. Babic A. Cloud computing in healthcare: A space of opportunities and challenges. Stud. Health Technol. Inform. 2016 221 122 27071896
    [Google Scholar]
  12. Griebel L. Prokosch H.U. Köpcke F. A scoping review of cloud computing in healthcare. BMC Med. Inform. Decis. Mak. 2015 15 1 17 10.1186/s12911‑015‑0145‑7 25888747
    [Google Scholar]
  13. Bonde B. Edge, fog, and cloud against disease: The potential of high-performance cloud computing for pharma drug discovery. Methods Mol. Biol. 2024 2716 181 202 10.1007/978‑1‑0716‑3449‑3_8 37702940
    [Google Scholar]
  14. Spjuth O. Frid J. Hellander A. The machine learning life cycle and the cloud: Implications for drug discovery. Expert Opin. Drug Discov. 2021 16 9 1071 1079 10.1080/17460441.2021.1932812 34057379
    [Google Scholar]
  15. Sarkar C. Das B. Rawat V.S. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int. J. Mol. Sci. 2023 24 3 2026 10.3390/ijms24032026 36768346
    [Google Scholar]
  16. Ogiela L. Ogiela M.R. Ko H. Intelligent data management and security in cloud computing. Sensors 2020 20 12 3458 10.3390/s20123458 32570956
    [Google Scholar]
  17. Pugazhenthi A. Chitra D. Data access control and secured data sharing approach for health care data in cloud environment. J. Med. Syst. 2019 43 8 258 10.1007/s10916‑019‑1381‑7 31264005
    [Google Scholar]
  18. Kumar Sharma D. Sreenivasa Chakravarthi D. Ara Shaikh A. Al Ayub Ahmed A. Jaiswal S. Naved M. The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique. Mater. Today Proc. 2023 80 Part 3 3805 3810 10.1016/j.matpr.2021.07.388
    [Google Scholar]
  19. Ali O. Shrestha A. Soar J. Wamba S.F. Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review. Int. J. Inf. Manage. 2018 43 146 158 10.1016/j.ijinfomgt.2018.07.009
    [Google Scholar]
  20. Chen P.T. Lin C.L. Wu W.N. Big data management in healthcare: Adoption challenges and implications. Int. J. Inf. Manage. 2020 53 102078 10.1016/j.ijinfomgt.2020.102078
    [Google Scholar]
  21. Sadoughi F. Erfannia L. Health information system in a cloud computing context. Stud. Health Technol. Inform. 2017 236 290 297 28508809
    [Google Scholar]
  22. Ahmadi M. Aslani N. Capabilities and advantages of cloud computing in the implementation of electronic health record. Acta Inform. Med. 2018 26 1 24 28 10.5455/aim.2018.26.24‑28 29719309
    [Google Scholar]
  23. Mehrtak M SeyedAlinaghi S MohsseniPour M Security challenges and solutions using healthcare cloud computing. J. Med. Life 2021 14 4 448 461 10.25122/jml‑2021‑0100 34621367
    [Google Scholar]
  24. Rehaimi A. Sadqi Y. Maleh Y. Gaba G.S. Gurtov A. Towards a federated and hybrid cloud computing environment for sustainable and effective provisioning of cyber security virtual laboratories. Exp Syst Applicat 2024 252 19 124267 10.1016/j.eswa.2024.124267
    [Google Scholar]
  25. Li H. Computer security issues and legal system based on cloud computing. Comput. Intell. Neurosci. 2022 2022 1 11 10.1155/2022/8112212 35860647
    [Google Scholar]
  26. Ryu A.J. Magnuson D.R. Kingsley T.C. Why mayo clinic is embracing the cloud and what this means for clinicians and researchers. Mayo Clin. Proc. Innov. Qual. Outcomes 2021 5 6 969 973 10.1016/j.mayocpiqo.2021.08.010 34632298
    [Google Scholar]
  27. Luthria G. Wang Q. Implementing a cloud based method for protected clinical trial data sharing. Pac. Symp. Biocomput. 2020 25 647 658 31797635
    [Google Scholar]
  28. Al-Issa Y. Ottom M.A. Tamrawi A. eHealth cloud security challenges: A survey. J. Healthc. Eng. 2019 2019 1 15 10.1155/2019/7516035 31565209
    [Google Scholar]
  29. Ilokah M. Eklund J.M. A secure privacy preserving cloud-based framework for sharing electronic health data. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020 2020 5592 5597 10.1109/EMBC44109.2020.9175792 33019245
    [Google Scholar]
  30. Hauser S.L. Johnston S.C. Global clinical trials: Challenges ahead. Ann. Neurol. 2011 70 4 A8 A9 10.1002/ana.22639 22028240
    [Google Scholar]
  31. Xie C.X. De Simoni A. Eldridge S. Pinnock H. Relton C. Development of a conceptual framework for defining trial efficiency. PLoS One 2024 19 5 0304187 10.1371/journal.pone.0304187 38781167
    [Google Scholar]
  32. Muvhulawa N. Dludla P.V. Ndlovu M. Global trends in clinical trials and interventions for the metabolic syndrome: A comprehensive analysis of the WHO International Clinical Trials platform. Contemp. Clin. Trials Commun. 2024 40 101330 10.1016/j.conctc.2024.101330 39070960
    [Google Scholar]
  33. Zhong X. Lin S. Deng M. Guan L. The global status of bioequivalence trials: A comprehensive clinical trial landscape analysis based on the Trialtrove database. Drug Discov. Today 2024 29 12 104223 10.1016/j.drudis.2024.104223 39515412
    [Google Scholar]
  34. Tedersoo L. Küngas R. Oras E. Data sharing practices and data availability upon request differ across scientific disciplines. Sci. Data 2021 8 1 192 10.1038/s41597‑021‑00981‑0 34315906
    [Google Scholar]
  35. Ronquillo J.G. Lester W.T. Practical aspects of implementing and applying health care cloud computing services and informatics to cancer clinical trial data. JCO Clin. Cancer Inform. 2021 5 5 826 832 10.1200/CCI.21.00018 34383582
    [Google Scholar]
  36. Ng C.E. Bowman S. Ling J. Bagshaw R. Birt A. Yiannakou Y. The future of clinical trials—is it virtual? Br. Med. Bull. 2023 148 1 42 57 10.1093/bmb/ldad022 37681298
    [Google Scholar]
  37. Hanley D.F. Bernard G.R. Wilkins C.H. Decentralized clinical trials in the trial innovation network: Value, strategies, and lessons learned. J. Clin. Transl. Sci. 2023 7 1 170 10.1017/cts.2023.597 37654775
    [Google Scholar]
  38. Ali Z. Zibert J.R. Thomsen S.F. Virtual clinical trials: Perspectives in dermatology. Dermatology 2020 236 4 375 382 10.1159/000506418 32126560
    [Google Scholar]
  39. Harrer S. Shah P. Antony B. Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 2019 40 8 577 591 10.1016/j.tips.2019.05.005 31326235
    [Google Scholar]
  40. Bordukova M. Makarov N. Rodriguez-Esteban R. Schmich F. Menden M.P. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin. Drug Discov. 2024 19 1 33 42 10.1080/17460441.2023.2273839 37887266
    [Google Scholar]
  41. Anjaneyan G. Kaliyadan F. Pandhi D. Sankar R. Virtual clinical trials–Implications for future dermatology research. Indian J. Dermatol. Venereol. Leprol. 2024 90 5 636 639 10.25259/IJDVL_1379_2023 39152846
    [Google Scholar]
  42. Hasselgren C. Oprea T.I. Artificial intelligence for drug discovery: Are we there yet? Annu. Rev. Pharmacol. Toxicol. 2024 64 1 527 550 10.1146/annurev‑pharmtox‑040323‑040828 37738505
    [Google Scholar]
  43. Gangwal A. Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov. Today 2024 29 6 103992 10.1016/j.drudis.2024.103992 38663579
    [Google Scholar]
  44. Thangaraj P.M. Benson S.H. Oikonomou E.K. Asselbergs F.W. Khera R. Cardiovascular care with digital twin technology in the era of generative artificial intelligence. Eur. Heart J. 2024 45 45 4808 4821 10.1093/eurheartj/ehae619 39322420
    [Google Scholar]
  45. Cunningham J.W. Abraham W.T. Bhatt A.S. Heart failure collaboratory. artificial intelligence in cardiovascular clinical trials. J. Am. Coll. Cardiol. 2024 84 20 2051 2062 10.1016/j.jacc.2024.08.069 39505413
    [Google Scholar]
  46. Lam T.Y.T. Cheung M.F.K. Munro Y.L. Lim K.M. Shung D. Sung J.J.Y. Randomized controlled trials of artificial intelligence in clinical practice: Systematic review. J. Med. Internet Res. 2022 24 8 37188 10.2196/37188 35904087
    [Google Scholar]
  47. Kang J. Chowdhry A.K. Pugh S.L. Park J.H. Integrating artificial intelligence and machine learning into cancer clinical trials. Semin. Radiat. Oncol. 2023 33 4 386 394 10.1016/j.semradonc.2023.06.004 37684068
    [Google Scholar]
  48. Ryan D.K. Maclean R.H. Balston A. Scourfield A. Shah A.D. Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br. J. Clin. Pharmacol. 2024 90 3 629 639 10.1111/bcp.15930 37845024
    [Google Scholar]
  49. Gupta R. Srivastava D. Sahu M. Tiwari S. Ambasta R.K. Kumar P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers. 2021 25 3 1315 1360 10.1007/s11030‑021‑10217‑3 33844136
    [Google Scholar]
  50. Iskander R. Moyer H. Vigneault K. Mahmud S.M. Kimmelman J. Survival benefit associated with participation in clinical trials of anticancer drugs. JAMA 2024 331 24 2105 2113 10.1001/jama.2024.6281 38767595
    [Google Scholar]
  51. Hurley C. Shiely F. Power J. Risk based monitoring (RBM) tools for clinical trials: A systematic review. Contemp. Clin. Trials 2016 51 15 27 10.1016/j.cct.2016.09.003 27641969
    [Google Scholar]
  52. Adams A. Adelfio A. Barnes B. Risk-based monitoring in clinical trials: 2021 update. Ther. Innov. Regul. Sci. 2023 57 3 529 537 10.1007/s43441‑022‑00496‑9 36622566
    [Google Scholar]
  53. Barnes B. Stansbury N. Brown D. Risk-based monitoring in clinical trials: Past, present, and future. Ther. Innov. Regul. Sci. 2021 55 4 899 906 10.1007/s43441‑021‑00295‑8 33914298
    [Google Scholar]
  54. Kimmelman J. Mandel D.R. Benjamin D.M. Predicting clinical trial results: A synthesis of five empirical studies and their implications. Perspect. Biol. Med. 2023 66 1 107 128 10.1353/pbm.2023.0006 38662011
    [Google Scholar]
  55. Carmeli A.B. Meloney L. Bierer B.E. Data visualization explorer: A tool for participant representation in pivotal trials of FDA-approved medicinal products. Patterns 2023 4 5 100713 10.1016/j.patter.2023.100713 37223273
    [Google Scholar]
  56. Wanderer J.P. Lasko T.A. Coco J.R. Visualization of aggregate perioperative data improves anesthesia case planning: A randomized, cross-over trial. J. Clin. Anesth. 2021 68 110114 10.1016/j.jclinane.2020.110114 33142248
    [Google Scholar]
  57. Holford N. Ma S.C. Ploeger B.A. Clinical trial simulation: A review. Clin. Pharmacol. Ther. 2010 88 2 166 182 10.1038/clpt.2010.114 20613720
    [Google Scholar]
  58. Li X. Liu S. Liu D. Yu M. Wu X. Wang H. Application of virtual drug study to new drug research and development: Challenges and opportunity. Clin. Pharmacokinet. 2024 63 9 1239 1249 10.1007/s40262‑024‑01416‑w 39225885
    [Google Scholar]
  59. Girard P. Cucherat M. Guez D. Clinical trial simulation in drug development. Therapie 2004 59 3 287 295, 297-304 10.2515/therapie:2004056 15559184
    [Google Scholar]
  60. Tovino S.A. The HIPAA privacy rule and the EU GDPR: Illustrative comparisons. Seton Hall Law Rev. 2017 47 4 973 993 28820562
    [Google Scholar]
  61. Bisson T. Franz M. Dogan O.I. Anonymization of whole slide images in histopathology for research and education. Digit. Health 2023 9 20552076231171475 10.1177/20552076231171475 37205164
    [Google Scholar]
  62. Spencer A. Patel S. Applying the data protection act 2018 and general data protection regulation principles in healthcare settings. Nurs. Manag. 2019 26 1 34 40 10.7748/nm.2019.e1806 31468753
    [Google Scholar]
  63. Upadrista V. Nazir S. Tianfield H. Secure data sharing with blockchain for remote health monitoring applications: A review. J. Reliab. Intell. Environ. 2023 9 3 349 368 10.1007/s40860‑023‑00204‑w 37359293
    [Google Scholar]
  64. Chico V. The impact of the general data protection regulation on health research. Br. Med. Bull. 2018 128 1 109 118 10.1093/bmb/ldy038 30445448
    [Google Scholar]
  65. de Carvalho Junior M.A. Bandiera-Paiva P. Health information system role-based access control current security trends and challenges. J. Healthc. Eng. 2018 2018 1 8 10.1155/2018/6510249
    [Google Scholar]
  66. Suleski T. Ahmed M. Yang W. Wang E. A review of multi-factor authentication in the Internet of Healthcare Things. Digit. Health 2023 9 20552076231177144 10.1177/20552076231177144 37252257
    [Google Scholar]
  67. Tiwari B. Kumar A. Role-based access control through on-demand classification of electronic health record. Int. J. Electron. Healthc. 2015 8 1 9 24 10.1504/IJEH.2015.071637 26559071
    [Google Scholar]
  68. Liu P.L. Ye J.F. Ao H.S. Effects of electronic personal health information technology on American women’s cancer screening behaviors mediated through cancer worry: Differences and similarities between 2017 and 2020. Digit. Health 2023 9 20552076231185271 10.1177/20552076231185271 37434732
    [Google Scholar]
  69. JPC Rodrigues J, de la Torre I, Fernández G, López-Coronado M. Analysis of the security and privacy requirements of cloud-based electronic health records systems. J. Med. Internet Res. 2013 15 8 186 10.2196/jmir.2494 23965254
    [Google Scholar]
  70. Kumar S. Basu M. Ghosh P. Ansari A. Ghosh M.K. COVID‐19: Clinical status of vaccine development to date. Br. J. Clin. Pharmacol. 2023 89 1 114 149 10.1111/bcp.15552 36184710
    [Google Scholar]
  71. Mitchell E.J. Goodman K. Wakefield N. Clinical trial management: A profession in crisis? Trials 2022 23 1 357 10.1186/s13063‑022‑06315‑8 35477835
    [Google Scholar]
  72. Berisha B. Mëziu E. Shabani I. Big data analytics in Cloud computing: An overview. J Cloud Comput 2022 11 1 24 10.1186/s13677‑022‑00301‑w 35966392
    [Google Scholar]
  73. Pathan S.R. Asarawala N. Sharma K.B. Cancer clinical trials: Advancing care, inspiring hope. Cureus 2024 16 4 57891 10.7759/cureus.57891 38725752
    [Google Scholar]
  74. Sankar K. Redman M.W. Dragnev K.H. Pragmaticism in cancer clinical trials. Am. Soc. Clin. Oncol. Educ. Book 2024 44 3 100040 10.1200/EDBK_100040 38771997
    [Google Scholar]
  75. Nabbout R. Hilgers R.D. Innovative methodologies for rare diseases clinical trials. Orphanet J. Rare Dis. 2024 19 1 190 10.1186/s13023‑024‑03189‑8 38715067
    [Google Scholar]
  76. Jacobson R.M. Pignolo R.J. Lazaridis K.N. Clinical trials for special populations: Children, older adults, and rare diseases. Mayo Clin. Proc. 2024 99 2 318 335 10.1016/j.mayocp.2023.03.003 38309939
    [Google Scholar]
  77. Abrahamyan L. Feldman B.M. Tomlinson G. Alternative designs for clinical trials in rare diseases. Am. J. Med. Genet. C. Semin. Med. Genet. 2016 172 4 313 331 10.1002/ajmg.c.31533 27862920
    [Google Scholar]
  78. Mellerio J.E. The challenges of clinical trials in rare diseases. Br. J. Dermatol. 2022 187 4 453 454 10.1111/bjd.21686 36192346
    [Google Scholar]
  79. Blumenrath S.H. Lee B.Y. Low L. Prithviraj R. Tagle D. Tackling rare diseases: Clinical trials on chips. Exp. Biol. Med. 2020 245 13 1155 1162 10.1177/1535370220924743 32397761
    [Google Scholar]
  80. Nair S.C. Satish K.P. Al Maini M. Ibrahim H. Optimizing patient safety in clinical trials by improving transitions of care. Jt. Comm. J. Qual. Patient Saf. 2020 46 4 232 233 10.1016/j.jcjq.2020.01.001 32057672
    [Google Scholar]
  81. Lorch U. Vincent C. The safety of early phase clinical trials: Bridging the gap between governance and practice. Br. J. Clin. Pharmacol. 2024 90 7 1538 1540 10.1111/bcp.16141 38880991
    [Google Scholar]
  82. Dal-Ré R. Clinical development of new drugs. Safety of clinical trials in healthy volunteers. Med. Clin. 2016 147 4 154 156 10.1016/j.medcli.2016.03.018
    [Google Scholar]
  83. Buyse M. Trotta L. Saad E.D. Sakamoto J. Central statistical monitoring of investigator-led clinical trials in oncology. Int. J. Clin. Oncol. 2020 25 7 1207 1214 10.1007/s10147‑020‑01726‑6 32577951
    [Google Scholar]
  84. Dodd LE Proschan MA Data and safety monitoring board monitoring of clinical trials for early efficacy. NEJM Evid 2022 1 3 EVIDctw2100025 10.1056/EVIDctw2100025
    [Google Scholar]
  85. El Mouelhi M. Drug development and challenges for neuromuscular clinical trials. J. Mol. Neurosci. 2016 58 3 374 378 10.1007/s12031‑015‑0700‑9 26691331
    [Google Scholar]
  86. Lagler F.B. Hirschfeld S. Kindblom J.M. Challenges in clinical trials for children and young people. Arch. Dis. Child. 2021 106 4 321 325 10.1136/archdischild‑2019‑318676 33077422
    [Google Scholar]
  87. Minneci P.C. Deans K.J. Clinical trials. Semin. Pediatr. Surg. 2018 27 6 332 337 10.1053/j.sempedsurg.2018.10.003 30473036
    [Google Scholar]
  88. Arai R.J. Guindalini R.S.C. Llera A.S. Personalizing precision oncology clinical trials in latin america: An expert panel on challenges and opportunities. Oncologist 2019 24 8 e709 e719 10.1634/theoncologist.2018‑0318 30910864
    [Google Scholar]
  89. Turner M.A. Kenny L. Alfirevic Z. Challenges in designing clinical trials to test new drugs in the pregnant woman and fetus. Clin. Perinatol. 2019 46 2 399 416 10.1016/j.clp.2019.02.015 31010567
    [Google Scholar]
  90. Kempf L. Goldsmith J.C. Temple R. Challenges of developing and conducting clinical trials in rare disorders. Am. J. Med. Genet. A. 2018 176 4 773 783 10.1002/ajmg.a.38413 28815894
    [Google Scholar]
  91. Mishra S. Venkatesh M.P. Rare disease clinical trials in the European Union: Navigating regulatory and clinical challenges. Orphanet J. Rare Dis. 2024 19 1 285 10.1186/s13023‑024‑03146‑5 39085891
    [Google Scholar]
  92. Nipp R.D. Hong K. Paskett E.D. Overcoming barriers to clinical trial enrollment. Am. Soc. Clin. Oncol. Educ. Book 2019 39 39 105 114 10.1200/EDBK_243729 31099636
    [Google Scholar]
  93. García-García I. Seco-Meseguer E. Borobia A.M. Carcas-Sansuán A.J. Implementing pharmacogenetics in clinical trials: Considerations about current methodological, ethical, and regulatory challenges. Expert Rev. Clin. Pharmacol. 2024 17 1 1 10 10.1080/17512433.2023.2293999 38088171
    [Google Scholar]
  94. Hung H.M.J. Wang S.J. Yang P. Statistical challenges in a regulatory review of cardiovascular and CNS clinical trials. J. Biopharm. Stat. 2016 26 1 37 43 10.1080/10543406.2015.1092025 26366624
    [Google Scholar]
  95. Castro C. Leiva V. Garrido D. Huerta M. Minatogawa V. Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics. Comput. Methods Programs Biomed. 2024 255 108321 10.1016/j.cmpb.2024.108321 39053350
    [Google Scholar]
  96. Zhuang Y. Sheets L. Gao X. Development of a blockchain framework for virtual clinical trials. AMIA Annu. Symp. Proc. 2021 2020 1412 1420 33936517
    [Google Scholar]
  97. Kushwaha P. Srivastava N. Kushwaha S.P. Enhancing clinical drug trial monitoring with blockchain technology. Contemp. Clin. Trials 2024 146 107684 10.1016/j.cct.2024.107684 39236782
    [Google Scholar]
  98. Maslove D.M. Klein J. Brohman K. Martin P. Using blockchain technology to manage clinical trials data: A proof-of-concept study. JMIR Med. Inform. 2018 6 4 11949 10.2196/11949 30578196
    [Google Scholar]
  99. Shah P. Patel C. Patel J. Shah A. Pandya S. Sojitra B. Utilizing blockchain technology for healthcare and biomedical research: A review. Cureus 2024 16 10 72040 10.7759/cureus.72040 39569280
    [Google Scholar]
  100. Li X. Zhao L. Xu T. Cardiac telerehabilitation under 5G internet of things monitoring: A randomized pilot study. Sci. Rep. 2023 13 1 18886 10.1038/s41598‑023‑46175‑z 37919385
    [Google Scholar]
  101. Chen Q. Wu X. Huang Y. Chen L. Internet of things-based home respiratory muscle training for patients with chronic obstructive pulmonary disease: A randomized clinical trial. Int. J. Chron. Obstruct. Pulmon. Dis. 2024 19 1093 1103 10.2147/COPD.S454804 38800522
    [Google Scholar]
  102. Lin B. Wu S. Digital transformation in personalized medicine with artificial intelligence and the internet of medical things. OMICS 2022 26 2 77 81 10.1089/omi.2021.0037 33887155
    [Google Scholar]
  103. Monteith S. Glenn T. Geddes J. Severus E. Whybrow P.C. Bauer M. Internet of things issues related to psychiatry. Int. J. Bipolar Disord. 2021 9 1 11 10.1186/s40345‑020‑00216‑y 33797634
    [Google Scholar]
  104. Manickam P Mariappan SA Murugesan SM Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. biosensors 2022 12 8 562 10.3390/bios12080562 35892459
    [Google Scholar]
  105. Kingsmore K.M. Lipsky P.E. Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus. Curr. Opin. Rheumatol. 2022 34 6 374 381 10.1097/BOR.0000000000000902 36001343
    [Google Scholar]
  106. Kolluri S. Lin J. Liu R. Zhang Y. Zhang W. Machine learning and artificial intelligence in pharmaceutical research and development: A review. AAPS J. 2022 24 1 19 10.1208/s12248‑021‑00644‑3 34984579
    [Google Scholar]
  107. Vamathevan J. Clark D. Czodrowski P. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 2019 18 6 463 477 10.1038/s41573‑019‑0024‑5 30976107
    [Google Scholar]
  108. Gomase V.S. Dhamane S.P. Kakade P.G. Immunoproteomics: A review of techniques, applications, and advancements. Protein Pept. Lett. 2024 31 11 827 849 10.2174/0109298665333029240926092919 39506417
    [Google Scholar]
  109. Gomase V.S. Dhamane S.P. Kemkar K.R. Kakade P.G. Sakhare A.D. Immunoproteomics: Approach to diagnostic and vaccine development. Protein Pept. Lett. 2024 31 10 773 795 10.2174/0109298665342261240912105111 39473104
    [Google Scholar]
/content/journals/rrct/10.2174/0115748871379249250701065507
Loading
Cloud Computing Facilitating Data Storage, Collaboration, and Analysis in Global Healthcare Clinical Trials
Bentham Science Publishers ; https://doi.org/10.2174/0115748871379249250701065507
/content/journals/rrct/10.2174/0115748871379249250701065507
/content/journals/rrct/10.2174/0115748871379249250701065507
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: clinical trial management ; regulatory compliance (HIPAA ; Cloud computing in healthcare ; global clinical trials ; GDPR) ; data storage solutions ; healthcare data analytics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test