cirpublications.com-UYsQ-1790878255-v392507026 1790878255 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 1 Ravi Kumar Neha Sharma Bruno Martins Lucas Pereira 2024 10.68159/v392507026 https://cirpublications.com/pub/journal/1/article/v392507026 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3(1):17. 10.1038/s41746-020-0221-y Howe JL, Adams KT, Hettinger AZ, Ratwani RM. Electronic health record usability issues and potential contribution to patient harm. JAMA. 2018;319(12):1276-8. 10.1001/jama.2018.1171 Holmgren AJ, Apathi M, Adler-Milstein J. Changes in physician electronic health record use with the expansion of telemedicine. JAMA Intern Med. 2023;183(12):1357-65. 10.1001/jamainternmed.2023.5738 Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar TM. Artificial intelligence in the field of pharmacy practice: a literature review. J King Saud Univ Sci. 2023;35(9):102965. 10.1016/j.jksus.2023.102965 Haleem A, Javaid M, Singh RP, Suman R. Medical 4.0 technologies for healthcare: features, capabilities, and applications. Internet Things Cyber-Phys Syst. 2022;2:12-30. 10.1016/j.iotcps.2022.04.001 Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform. 2020;24(7):1837-57. 10.1109/JBHI.2020.2991043 Yang G, Ye Q, Xia J. Homecare robotic systems for healthcare 4.0: visions and enabling technologies. IEEE J Biomed Health Inform. 2020;24(9):2535-49. 10.1109/JBHI.2020.2990529 Suraci C, Mastronardi A, Rizzuti F, Russo E, Vacca M, De Sarro G, et al. The next generation of eHealth: a multidisciplinary survey. IEEE Access. 2022;10:118308-32. 10.1109/ACCESS.2022.3219813 Mc Cord KA, Eslami S, Schuit E, Bonten MJM, Roes KCB. Using electronic health records for clinical trials: where do we stand and where can we go? CMAJ. 2019;191(5):E128-E133. 10.1503/cmaj.180841 Mason M, Cho Y, Rayo J, Gong Y, Harris B, Rowe D. Technologies for medication adherence monitoring and technology assessment criteria: narrative review. JMIR Mhealth Uhealth. 2022;10(3):e35157. 10.2196/35157 Withall JB, Schwartz J, Garg A, Bozeman C, Cassidy D, Devarajan V, et al. A scoping review of integrated medical devices and clinical decision support in the acute care setting. Appl Clin Inform. 2022;13(5):1223-36. 10.1055/s-0042-1759519 Kumar S, Mallipeddi RR. Impact of cybersecurity on operations and supply chain management: emerging trends and future research directions. Prod Oper Manag. 2022;31(12):4488-500. Nasir K, Javed Z, Yadav S, Choudhry M, Michaels C, Schechter A, et al. The promise of big data and digital solutions in building a cardiovascular learning health system: opportunities and barriers. Methodist Debakey Cardiovasc J. 2020;16(3):212-9. 10.14797/mdcj-16-3-212 Seaberg RW, Seaberg DC, Seaberg P, Johnston D, Miller B. Use of blockchain technology for electronic prescriptions. Blockchain Healthc Today. 2021;4:183. 10.30953/bhty.v4.183 Tromp J, Jindal D, Redfern J, Bhatt A, Séverin T, Banerjee A, et al. World Heart Federation roadmap for digital health in cardiology. Glob Heart. 2022;17(1):61. 10.5334/gh.1141 Balch JA, Wirtz EC, Panchal AR, Nesheiwat L, Lee M, Bachmann DJ, et al. Machine learning–enabled clinical information systems using fast healthcare interoperability resources data standards: scoping review. JMIR Med Inform. 2023;11:e48297. 10.2196/48297 Yaeger K, Martini M, Yan J, Al Kasab S, Harvey J, Sandhu I. Emerging blockchain technology solutions for modern healthcare infrastructure. J Sci Innov Med. 2019;2(1):1. 10.29024/jsim.7 Gurbeta Pokvić L, Spahić L, Badnjević A. “Democratizing” artificial intelligence in medicine and healthcare: mapping the uses of machine learning in low- and middle-income countries. Front Genet. 2022;13:902542. 10.3389/fgene.2022.902542 Ehwerhemuepha L, Gasperino G, Bischoff N, Feaster W, Inman A, Kranjac D, et al. HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric intensive care unit mortality. SN Appl Sci. 2020;2(1):4. 10.1007/s42452-019-1887-5 Fürstenau D, Klein S, Bärr A, Gersch M. Digital therapeutics (DTx) – an overview of an emerging field in the healthcare industry. Bus Inf Syst Eng. 2023;65(3):349-59. 10.1007/s12599-023-00804-z Sharma A, Sharma L, Sahoo PK. Exploring the need and benefits of digital therapeutics (DTx) for the management of heart failure in India. Cureus. 2023;15(11):e49556. 10.7759/cureus.49556 Babel A, Taneja R, Mondello Malvestiti F, Monaco A, Donde S. Artificial intelligence solutions to increase medication adherence in patients with non-communicable diseases. Front Digit Health. 2021;3:669869. 10.3389/fdgth.2021.669869 Lo-Ciganic WH, Huang JL, Zhang HH, Weiss JC, Wu Y, Kwoh CK, et al. Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: a prognostic study. PLoS One. 2020;15(7):e0235981. 10.1371/journal.pone.0235981 Asgary R, Garland V, Roze des Ordons A, Currie G, Stelfox HT. Using artificial intelligence to better predict and develop biomarkers for immune checkpoint inhibitor treatment in melanoma. Mayo Clin Proc. 2019;94(8):1611-3. 10.1016/j.mayocp.2019.06.002 D’Errico S, Zanon M, Radaelli D, Padovano M, Santurro A, Scopetti M, et al. Medication errors in pediatrics: proposals to improve the quality and safety of care through clinical risk management. Front Med (Lausanne). 2022;8:814100. Kocaballi AB, Ijaz K, Laranjo L, Quiroz JC, Rezazadegan D, Tong HL, et al. Envisioning an artificial intelligence documentation assistant for future primary care consultations: a co-design study with general practitioners. J Am Med Inform Assoc. 2020;27(11):1695-704. Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154(11):1247-8. 10.1001/jamadermatol.2018.2348 Yamauchi T, Ochi D, Matsukawa N, Saigusa D, Ishikuro M, Obara T, et al. Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis. Sci Rep. 2021;11(1):17777.