{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:53:39Z","timestamp":1767650019480,"version":"3.41.2"},"reference-count":108,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Healthcare sector has become one of the challenging sectors to handle patient records as well as to provide better treatment to patients within a limited period. Covid-19 also exposed the limitations of the healthcare system due to the lack of better services. So, the involvement of information and communication technologies (ICTs) with the healthcare sector brings radical changes at global as well as local levels such as in hospitals and dispensaries. The article enlightened a novel survey technological paradigm that helps to facilitate the digital healthcare. With the use of technologies, the healthcare sectors are becoming more digital, innovative, patient-centric, and more effective. This article explores the proposed technological developments such as real-time health monitoring, generation of electronic health records, patient health record, mhealth, robotics, as well as robot sensors that are associated with healthcare sectors. This article also highlights the role of ICTs in different healthcare-related fields such as education, hospital management, health-related research, and data management as well as lightening the delivery levels of healthcare services. The article deals with the robotic applications in the healthcare field. This article categorizes the technologies as current and futuristic technological innovations enabling healthcare-as-a-service with benefits.<\/jats:p>","DOI":"10.1515\/pjbr-2022-0108","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T15:05:56Z","timestamp":1690383956000},"source":"Crossref","is-referenced-by-count":1,"title":["Digital healthcare: A topical and futuristic review of technological and robotic revolution"],"prefix":"10.1515","volume":"14","author":[{"family":"Shilpa","sequence":"first","affiliation":[{"name":"School of Computer Applications, Lovely Professional University , Phagwara , Punjab 144411 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarandeep","family":"Kaur","sequence":"additional","affiliation":[{"name":"School of Computer Applications, Lovely Professional University , Phagwara , Punjab 144411 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachit","family":"Garg","sequence":"additional","affiliation":[{"name":"School of Computer Applications, Lovely Professional University , Phagwara , Punjab 144411 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"2025073006061527313_j_pjbr-2022-0108_ref_001","unstructured":"K. Milioris and K. Papageorgiou, \u201cA study of healthcare ICT systems and their usefulness during Covid-19 focused in the European environment,\u201d J. Hosp. Health Care Admin. vol. 4, p. 149, 2021. 10.29011\/2688-6472.000149."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_002","doi-asserted-by":"crossref","unstructured":"N. Navaz, M. A. Serhani, H. T. El Kassabi, N. Al-Qirim, and H. Ismail, \u201cTrends, technologies, and key challenges in smart and connected healthcare,\u201d IEEE Access, vol. 9, pp. 74044\u201374067, 2021. 10.1109\/ACCESS.2021.3079217.","DOI":"10.1109\/ACCESS.2021.3079217"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_003","doi-asserted-by":"crossref","unstructured":"K. T. Shilpa, \u201cDigital healthcare: current trends, challenges and future perspectives,\u201d In: Proceedings of the Future Technologies Conference (FTC) 2021, Volume 2. FTC 2021. Lecture Notes in Networks and Systems, K. Arai, Ed., vol. 359, Cham, Springer, 2022. 10.1007\/978-3-030-89880-9_48.","DOI":"10.1007\/978-3-030-89880-9_48"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_004","unstructured":"Reinventing & Redefining Technology. https:\/\/www.manoramahealthcare.com\/technologies.php."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_005","unstructured":"How the \u2018Healthcare-as-a-Service\u2019 concept works as a business and care model. 2021. https:\/\/www.businessinsider.com\/sc\/how-healthcare-as-a-service-can-improve-the-health-system 2021-5?IR=T."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_006","unstructured":"Emerging Technology Trends Shaping the Healthcare Industry. 2019. https:\/\/blog.relecura.com\/2019\/02\/emerging-technology-trends-shaping-the-healthcare-industry\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_007","doi-asserted-by":"crossref","unstructured":"G. Rouleau, M. P. Gagnon and J. C\u00f4t\u00e9, \u201cImpacts of information and communication technologies on nursing care: an overview of systematic reviews (protocol),\u201d Syst. Rev., vol. 4, p. 75, 2015. 10.1186\/s13643-015-0062-y.","DOI":"10.1186\/s13643-015-0062-y"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_008","unstructured":"ICT in Health Care. http:\/\/pmssymohfw.nic.in\/index1.php? lang = 1&level = 1&sublinkid = 23&lid = 50."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_009","unstructured":"4 Ways Technology Is Improving Patient Safety. 2017. https:\/\/www.healthitoutcomes.com\/doc\/waystechnology-improving-patient-safety-0001."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_010","unstructured":"How to Improve Care Coordination With Technology. 2017. https:\/\/www.texturehealth.com\/blog\/how-toimprove-care-coordination-with-technology."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_011","unstructured":"How To Reduce Healthcare Cost Using Technology. 2021. https:\/\/www.tripleaimsoftware.com\/how-toreduce-healthcare-cost-using-technology\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_012","doi-asserted-by":"crossref","unstructured":"K. C. Tseng and C. C. Wu, \u201cAn expert fitness diagnosis system based on elastic cloud computing,\u201d Sci. World J., vol. 2014, 2014.","DOI":"10.1155\/2014\/981207"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_013","doi-asserted-by":"crossref","unstructured":"S. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K. S. Kwak, \u201cThe internet of things for health care: a comprehensive survey,\u201d IEEE Access, vol. 3, pp. 678\u2013708, 2015.","DOI":"10.1109\/ACCESS.2015.2437951"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_014","doi-asserted-by":"crossref","unstructured":"G. Wolgast, C. Ehrenborg, A. Israelsson, J. Helander, E. Johansson, and H. Manefjord, \u201cWireless body area network for heart attack detection [education corner],\u201d IEEE Antennas Propag. Mag., vol. 58, no. 5, pp. 84\u201392, Oct. 2016.","DOI":"10.1109\/MAP.2016.2594004"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_015","doi-asserted-by":"crossref","unstructured":"P. Gope and T. Hwang, \u201cBSN-Care: A secure IoT-based modern healthcare system using body sensor network,\u201d IEEE Sens. J., vol. 16, no. 5, pp. 1368\u20131376, 2016.","DOI":"10.1109\/JSEN.2015.2502401"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_016","doi-asserted-by":"crossref","unstructured":"Z. U. Ahmed, M. G. Mortuza, M. J. Uddin, M. H. Kabir, M. Mahiuddin, and M. J. Hoque, \u201cInternet of things based patient health monitoring system using wearable biomedical device,\u201d In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), IEEE, 2018, December, 1\u20135.","DOI":"10.1109\/CIET.2018.8660846"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_017","doi-asserted-by":"crossref","unstructured":"J. Saha, A. K. Saha, A. Chatterjee, S. Agrawal, A. Saha, A. Kar, et al., \u201cAdvanced IOT based combined remote health monitoring, home automation and alarm system,\u201d 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018. 10.1109\/ccwc.2018.8301659.","DOI":"10.1109\/CCWC.2018.8301659"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_018","unstructured":"Why Education Matters to Health: Exploring the Causes. 2015. https:\/\/societyhealth.vcu.edu\/work\/theprojects\/why-education-matters-to-health-exploring-the-causes.html."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_019","unstructured":"Information Communication Technology in HealthCare. 2018. https:\/\/www.frontenders.in\/blog\/information-communication-technology-healthcare.html."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_020","unstructured":"https:\/\/www.forbes.com\/sites\/forbesbusinesscouncil\/2021\/11\/18\/digital-transformation-trends-inhealthcare-to-watch-in-2021\/?sh=f6297e557ef5."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_021","unstructured":"Digital Transformation in Healthcare in 2021: 7 Key Trends. 2021. https:\/\/www.digitalauthority.me\/resources\/state-of-digital-transformation-healthcare\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_022","unstructured":"Accessed On. https:\/\/www.osplabs.com\/insights\/the-who-what-why-and-how-of-healthcarecloud-strategy\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_023","doi-asserted-by":"crossref","unstructured":"K. T. Shilpa, \u201cBlockchain and cloud technology: Leading the ICT innovations,\u201d In: ICT Systems and Sustainability. Lecture Notes in Networks and Systems, M. Tuba, S. Akashe, A. Joshi, Eds., Singapore, Springer, 2022, vol. 321. 10.1007\/978-981-16-5987-4_41.","DOI":"10.1007\/978-981-16-5987-4_41"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_024","doi-asserted-by":"crossref","unstructured":"L. M. Dang, M. J. Piran, D. Han, K. Min, and H. Moon, \u201cA survey on internet of things and cloud computing for healthcare,\u201d Electronics, vol. 8, no. 7. p. 768, 2019. 10.3390\/electronics8070768.","DOI":"10.3390\/electronics8070768"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_025","unstructured":"P. K. Bollineni and K. Neupane, \u201cImplications for adopting cloud computing in e-Health,\u201d Lambert Academic Publishing, Saarbr\u00fccken, 2011."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_026","doi-asserted-by":"crossref","unstructured":"O. Ali, A. Shrestha, J. Soar, and S. F. Wamba, \u201cCloud computing-enabled healthcare opportunities, issues, and applications: A systematic review,\u201d Int. J. Inf. Manag., vol. 43, pp. 146\u2013158, 2018.","DOI":"10.1016\/j.ijinfomgt.2018.07.009"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_027","unstructured":"https:\/\/theiotmagazine.com\/iot-in-healthcare-how-it-improves-medical-software-4ca703ea1130."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_028","unstructured":"A. S. Abdulbaki, S. A. D. M. Najim, and S. A. Khadim, \u201cEczema disease detection and recognition in cloud computing,\u201d Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 14396\u201314402, 2017."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_029","doi-asserted-by":"crossref","unstructured":"P. M. Kumar, S. Lokesh, R. Varatharajan, G. Chandra Babu, and P. Parthasarathy, \u201cCloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier,\u201d Future Gener. Comput. Syst., vol. 86, pp. 527\u2013534, 2018.","DOI":"10.1016\/j.future.2018.04.036"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_030","doi-asserted-by":"crossref","unstructured":"H. Xia, I. Asif and X. Zhao, \u201cCloud-ECG for real time ECG monitoring and analysis,\u201d Comput. Methods Prog. Biomed., vol. 110, no. 3, pp. 253\u2013259, 2013. 10.1016\/j.cmpb.2012.11.008.","DOI":"10.1016\/j.cmpb.2012.11.008"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_031","unstructured":"N. Sahanaa Sree and N. Banupriya, \u201cA cloud based risk prediction of coronary heart disease,\u201d Int. J. Appl. Eng., vol. 13, no. 5, pp. 2786\u20132790, 2018."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_032","doi-asserted-by":"crossref","unstructured":"N. Gupta, N. Ahuja, S. Malhotra, A. Bala, and G. Kaur, \u201cIntelligent heart disease prediction in cloud environment through ensembling,\u201d Expert. Syst., vol. 34, no. 3. p. e12207, 2017. 10.1111\/exsy.12207.","DOI":"10.1111\/exsy.12207"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_033","doi-asserted-by":"crossref","unstructured":"V. Aswin and S. Deepak, \u201cMedical diagnostics using cloud computing with fuzzy logic and uncertainty factors,\u201d 2012 International Symposium on Cloud and Services Computing, Mangalore, India, 2012. 10.1109\/iscos.2012.29.","DOI":"10.1109\/ISCOS.2012.29"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_034","doi-asserted-by":"crossref","unstructured":"V. Lahoura, H. Singh, A. Aggarwal, B. Sharma, M. A. Mohammed, R. Dama\u0161evi\u010dius, et al., \u201cCloud computing-based framework for breast cancer diagnosis using extreme learning machine,\u201d Diagnostics, vol. 11, p. 241, 2021. 10.3390\/diagnostics11020241.","DOI":"10.3390\/diagnostics11020241"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_035","doi-asserted-by":"crossref","unstructured":"M. A. Khan, S. Abbas, A. Atta, A. Ditta, H. Alquhayz, M. F. Khan, et al., \u201cIntelligent cloud based heart disease prediction system empowered with supervised machine learning,\u201d Comput. Mater. Continua, vol. 65, no. 1, pp. 139\u2013151, 2020.","DOI":"10.32604\/cmc.2020.011416"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_036","doi-asserted-by":"crossref","unstructured":"F. Khan, M. A. Khan, S. Abbas, A. Athar, S. Y. Siddiqui, A. H. Khan, et al., \u201cCloud-based breast cancer prediction empowered with soft computing approaches,\u201d J. Healthc. Eng., vol. 2020, pp. 1\u201316, 2020.","DOI":"10.1155\/2020\/8017496"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_037","unstructured":"https:\/\/www.digitalauthority.me\/resources\/big-data-in-healthcare\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_038","unstructured":"https:\/\/www.optisolbusiness.com\/insight\/importance-of-big-data-in-healthcare."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_039","doi-asserted-by":"crossref","unstructured":"N. Zhu, T. Diethe, M. Camplani, L. Tao, A. Burrows, N. Twomey, et al., \u201cBridging e-health and the internet of things: The SPHERE project,\u201d IEEE Intell. Syst., vol. 30, no. 4, pp. 39\u201346, 2015. 10.1109\/mis.2015.57.","DOI":"10.1109\/MIS.2015.57"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_040","doi-asserted-by":"crossref","unstructured":"P. Verma and S. K. Sood, \u201cCloud-centric IoT based disease diagnosis healthcare framework,\u201d J. Parallel Distrib. Comput., vol. 116, pp. 27\u201338, 2018. 10.1016\/j.jpdc.2017.11.018.","DOI":"10.1016\/j.jpdc.2017.11.018"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_041","doi-asserted-by":"crossref","unstructured":"I. Raeesi Vanani and M. Amirhosseini, \u201cIoT-based diseases prediction and diagnosis system for healthcare,\u201d In: Internet of Things for Healthcare Technologies. Studies in Big Data, C. Chakraborty, A. Banerjee, M. Kolekar, L. Garg, B. Chakraborty, Eds., vol. 73, Singapore, Springer, 2021. 10.1007\/978-981-15-4112-4_2.","DOI":"10.1007\/978-981-15-4112-4_2"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_042","doi-asserted-by":"crossref","unstructured":"R. Ani, S. Krishna, N. Anju, M. S. Aslam, and O. S. Deepa, \u201cIot based patient monitoring and diagnostic prediction tool using ensemble classifier,\u201d 2017 International Conference on Advances in Computing, Communication, 2017.","DOI":"10.1109\/ICACCI.2017.8126068"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_043","doi-asserted-by":"crossref","unstructured":"A. M. Ghosh, D. Halder, and S. K. A. Hossain, \u201cRemote health monitoring system through IoT,\u201d 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016. 10.1109\/iciev.2016.7760135.","DOI":"10.1109\/ICIEV.2016.7760135"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_044","doi-asserted-by":"crossref","unstructured":"S. Kale, S. Mane, and P. Patil, \u201cIOT based wearable biomedical monitoring system,\u201d 2017 International Conference on Trends in Electronics and Informatics (ICEI), 2017. 10.1109\/icoei.2017.8300852.","DOI":"10.1109\/ICOEI.2017.8300852"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_045","doi-asserted-by":"crossref","unstructured":"P. Kaur, R. Kumar, and M. Kumar, \u201cA healthcare monitoring system using random forest and internet of things (IoT),\u201d Multimed. Tools Appl., vol. 78, no. 14, pp. 19905\u201319916, 2019.","DOI":"10.1007\/s11042-019-7327-8"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_046","doi-asserted-by":"crossref","unstructured":"L. K. Sahu, P. K. Vyas, V. Soni, and A. Deshpande, \u201cSurvey of recent studies on healthcare technologies and computational intelligence approaches and their applications,\u201d In: Computational Intelligence and Applications for Pandemics and Healthcare, IGI Global, 2022, pp. 282\u2013307.","DOI":"10.4018\/978-1-7998-9831-3.ch014"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_047","unstructured":"https:\/\/data-flair.training\/blogs\/machine-learning-in-healthcare\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_048","doi-asserted-by":"crossref","unstructured":"R. Venkatesh, C. Balasubramanian, and M. Kaliappan, \u201cDevelopment of big data predictive analytics model for disease prediction using machine learning technique,\u201d J. Med. Syst., vol. 43, p. 272, 2019. 10.1007\/s10916-019-1398-y.","DOI":"10.1007\/s10916-019-1398-y"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_049","doi-asserted-by":"crossref","unstructured":"R. G. Saboji, \u201cA scalable solution for heart disease prediction using classification mining technique,\u201d 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017. 10.1109\/icecds.2017.8389755.","DOI":"10.1109\/ICECDS.2017.8389755"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_050","doi-asserted-by":"crossref","unstructured":"S. Mohan, C. Thirumalai, and G. Srivastava, \u201cEffective heart disease prediction using hybrid machine learning techniques,\u201d IEEE Access, vol. 7, pp. 81542\u201381554, 2019.","DOI":"10.1109\/ACCESS.2019.2923707"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_051","doi-asserted-by":"crossref","unstructured":"A. Corsi, F. F. de Souza, R. N. Pagani, and J. L. Kovaleski, \u201cBig data analytics as a tool for fighting pandemics: a systematic review of literature,\u201d J. Ambient. Intell. Humanized Comput., vol. 12, no. 10, pp. 1\u201318, 2020.","DOI":"10.1007\/s12652-020-02617-4"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_052","doi-asserted-by":"crossref","unstructured":"C. Pasupathi and V. Kalavakonda, \u201cEvidence Based health care system using Big Data for disease diagnosis,\u201d 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2016. 10.1109\/aeeicb.2016.7538393.","DOI":"10.1109\/AEEICB.2016.7538393"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_053","unstructured":"A. A. Mohammed, R. Basa, A. K. Kuchuru, S. P. Nandigama, and M. Gangolla, \u201cRandom forest machine learning technique to predict heart disease,\u201d Eur. J. Mol. Clin. Med., vol. 7, no. 4. p. 2020, 2020."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_054","doi-asserted-by":"crossref","unstructured":"S. H. Koppad and A. Kumar, \u201cApplication of big data analytics in healthcare system to predict COPD,\u201d 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016. 10.1109\/iccpct.2016.7530248.","DOI":"10.1109\/ICCPCT.2016.7530248"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_055","doi-asserted-by":"crossref","unstructured":"N. Das, L. Das, S. S. Rautaray, and M. Pandey, \u201cDetection and prevention of hiv aids using big data tool,\u201d In: 2018 3rd International Conference for Convergence in Technology (I2CT), IEEE, 2018 April, 1\u20135.","DOI":"10.1109\/I2CT.2018.8529703"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_056","doi-asserted-by":"crossref","unstructured":"C. A. Alexander and L. Wang, \u201cBig data analytics in heart attack prediction,\u201d J. Nurs. Care, vol. 6, no. 2, 2017. 10.4172\/2167-1168.1000393.","DOI":"10.4172\/2167-1168.1000393"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_057","unstructured":"A. Ismail, S. Abdlerazek, and I. M. El-Henawy, \u201cBig data analytics in heart diseases prediction,\u201d J. Theor. Appl. Inf. Technol., vol. 98, no. 11, pp. 15\u201319, 2020."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_058","doi-asserted-by":"crossref","unstructured":"P. Singh, A. Kaur, R. S. Batth, S. Kaur, and G. Gianini, \u201cMulti-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system,\u201d Neural Comput. Appl., vol. 33, no. 16, pp. 1\u201312, 2021.","DOI":"10.1007\/s00521-021-05798-x"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_059","unstructured":"https:\/\/www.practicebuilders.com\/blog\/4-ways-blockchain-is-revolutionizing-healthcare\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_060","doi-asserted-by":"crossref","unstructured":"R. Ben Fekih and M. Lahami, \u201cApplication of blockchain technology in healthcare: A comprehensive study,\u201d The Impact of Digital Technologies on Public Health in Developed and Developing Countries: 18th International Conference, ICOST 2020, Hammamet, Tunisia, June 24\u201326, 2020, Proceedings, vol. 12157, 2020, pp. 268\u2013276. 10.1007\/978-3-030-51517-1_23.","DOI":"10.1007\/978-3-030-51517-1_23"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_061","doi-asserted-by":"crossref","unstructured":"R. El-Bialy, M. A. Salamay, O. H. Karam, and M. E. Khalifa, \u201cFeature analysis of coronary artery heart disease data sets,\u201d Procedia Comput. Sci., vol. 65, pp. 459\u2013468, 2015. 10.1016\/j.procs.2015.09.132.","DOI":"10.1016\/j.procs.2015.09.132"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_062","unstructured":"S. Ghosh, Application of various data mining techniques to classify heart diseases, Doctoral dissertation, Dublin, National College of Ireland, 2017."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_063","doi-asserted-by":"crossref","unstructured":"J. Xia, H. Chen, Q. Li, M. Zhou, L. Chen, Z. Cai, et al., \u201cUltrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach,\u201d Comput. Methods Prog. Biomed., vol. 147, pp. 37\u201349, 2017. 10.1016\/j.cmpb.2017.06.005.","DOI":"10.1016\/j.cmpb.2017.06.005"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_064","doi-asserted-by":"crossref","unstructured":"K. Sultan, I. Naseer, R. Majeed, D. Musleh, M. A. S. Gollapalli, S. Chabani, and M. A. Khan, \u201cSupervised machine learning-based prediction of COVID-19,\u201d Comput., Mater. Continua, vol. 69, no. 1, pp. 21\u201334, 2021.","DOI":"10.32604\/cmc.2021.013453"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_065","doi-asserted-by":"crossref","unstructured":"D. Dahiwade, G. Patle, and E. Meshram, \u201cDesigning disease prediction model using machine learning approach,\u201d 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019. 10.1109\/iccmc.2019.8819782.","DOI":"10.1109\/ICCMC.2019.8819782"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_066","doi-asserted-by":"crossref","unstructured":"D. Shah, S. Patel, and S. K. Bharti, \u201cHeart disease prediction using machine learning techniques,\u201d SN Comput. Sci., vol. 1, no. 6, pp. 1\u20136, 2020.","DOI":"10.1007\/s42979-020-00365-y"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_067","doi-asserted-by":"crossref","unstructured":"N. L. Fitriyani, M. Syafrudin, G. Alfian, and J. Rhee, \u201cDevelopment of disease prediction model based on ensemble learning approach for diabetes and hypertension,\u201d IEEE Access, vol. 7, pp. 144777\u2013144789, 2019. 10.1109\/ACCESS.2019.2945129.","DOI":"10.1109\/ACCESS.2019.2945129"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_068","unstructured":"H. D. Masethe and M. A. Masethe, \u201cPrediction of heart disease using classification algorithms,\u201d In: World Congress on Engineering and Computer Science 2014 Vol II WCECS 2014, San Francisco, USA, 2014, 22\u201324 Oct."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_069","doi-asserted-by":"crossref","unstructured":"M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, \u201cDisease prediction by machine learning over big data from healthcare communities,\u201d IEEE Access, vol. 5, pp. 8869\u20138879, 2017. 10.1109\/ACCESS.2017.2694446.","DOI":"10.1109\/ACCESS.2017.2694446"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_070","doi-asserted-by":"crossref","unstructured":"S. Aftab, S. Alanazi, M. Ahmad, M. A. Khan, A. Fatima, and N. S. Elmitwally, \u201cCloud-based diabetes decision support system using machine learning fusion,\u201d Comput. Mater. Continua, vol. 68, no. 1, pp. 1341\u20131357, 2021.","DOI":"10.32604\/cmc.2021.016814"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_071","doi-asserted-by":"crossref","unstructured":"S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, \u201cThe role of artificial intelligence in healthcare: a structured literature review,\u201d BMC Med. Inf. Decis. Mak., vol. 21, p. 125, 2021. 10.1186\/s12911-021-01488-9.","DOI":"10.1186\/s12911-021-01488-9"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_072","unstructured":"https:\/\/www.delveinsight.com\/blog\/robotics-in-healthcare."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_073","unstructured":"M. Butter, A. Rensma, S. Kalisingh, M. Schoone, M. Leis, G. J. Gelderblom, et al., Robotics for healthcare, European Commission EC, Netherlands Organization for Applied Scientific Research (TNO), 2008."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_074","unstructured":"https:\/\/royaljay.com\/healthcare\/neural-networks-inhealthcare\/#:\u223c:text = Neural%20networks%20can%20be%20seen%20in%20most%20places,profes sionals%20discover%20safer%20and%20more%20effective%20medicines%20fast."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_075","doi-asserted-by":"crossref","unstructured":"P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry, and Y. Nam, \u201cBlockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing,\u201d IEEE Access, vol. 9, pp. 45706\u201345720, 2021. 10.1109\/ACCESS.2021.3065440.","DOI":"10.1109\/ACCESS.2021.3065440"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_076","doi-asserted-by":"crossref","unstructured":"T. Frikha, A. Chaari, F. Chaabane, O. Cheikhrouhou, and A. Zaguia, \u201cHealthcare and fitness data management using the IoT-based blockchain platform,\u201d J. Healthc. Eng., vol. 2021, p. 9978863, 12 pages, 2021. 10.1155\/2021\/9978863.","DOI":"10.1155\/2021\/9978863"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_077","doi-asserted-by":"crossref","unstructured":"A. Azaria, A. Ekblaw, T. Vieira, and A. Lippman, MedRec: Using blockchain for medical data access and permission management, 2016 2nd International Conference on Open and Big Data (OBD), 2016. 10.1109\/obd.2016.11.","DOI":"10.1109\/OBD.2016.11"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_078","doi-asserted-by":"crossref","unstructured":"G. Jain and A. Jain, Applications of AI, IoT, and robotics in healthcare service based on several aspects, In: Blockchain technology in healthcare applications, CRC Press, Florida, USA, 2022, pp. 87\u2013114.","DOI":"10.1201\/9781003224075-5"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_079","unstructured":"N. Pavithra and D. N. Afza, \u201cIssues and challenges in adopting robotics in healthcare-A conceptual study,\u201d J. Posit. Sch. Psychol., vol. 6, no. 8, pp. 4266\u20134270, 2022."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_080","unstructured":"https:\/\/www.dailypioneer.com\/2022\/columnists\/robotics-is-changing--healthcare.html#:\u223c:text = Application%20of%20robotics%20in%20healthcare,patients%20with%20long-term%20conditions."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_081","doi-asserted-by":"crossref","unstructured":"A. Joseph, B. Christian, A. A. Abiodun, and F. Oyawale, \u201cA review on humanoid robotics in healthcare,\u201d In: MATEC Web of Conferences, Vol. 153, EDP Sciences, 2018, p. 02004.","DOI":"10.1051\/matecconf\/201815302004"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_082","unstructured":"https:\/\/www.dell.com\/en-us\/blog\/healthcare-trends-in-neural-networks\/."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_083","doi-asserted-by":"crossref","unstructured":"D. Lavanya and K. U. Rani, \u201cPerformance evaluation of decision tree classifiers on medical datasets,\u201d Int. J. Comput. Appl., vol. 26, no. 4, pp. 1\u20134, 2011.","DOI":"10.5120\/3095-4247"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_084","unstructured":"R. Chowdhury, M. Chatterjee, and R. Samanta, \u201cAn artificial neural network model for neonatal disease diagnosis,\u201d Int. J. Artif. Intell. Expert. Syst. (IJAE), vol. 2, no. 3, pp. 96\u2013106, 2011."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_085","unstructured":"B. Zebardast, A. Ghaffari, and M. Masdari, \u201cA new generalized regression artificial neural networks approach for diagnosing heart disease,\u201d Int. J. Innov. Appl. Stud., vol. 4, no. 4, pp. 679\u2013689, 2013."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_086","doi-asserted-by":"crossref","unstructured":"C. B. Sivaparthipan, B. A. Muthu, G. Manogaran, B. Maram, R. Sundarasekar, S. Krishnamoorthy, et al., \u201cInnovative and efficient method of robotics for helping the Parkinson\u2019s disease patient using IoT in big data analytics,\u201d Trans. Emerg. Telecommun. Technol., vol. 31, no. 12. p. e3838, 2020.","DOI":"10.1002\/ett.3838"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_087","doi-asserted-by":"crossref","unstructured":"J. Mohana, B. Yakkala, S. Vimalnath, P. M. Benson Mansingh, N. Yuvaraj, K. Srihari, et al., \u201cApplication of internet of things on the healthcare field using convolutional neural network processing,\u201d J. Healthc. Eng., vol. 2022, p. 1892123, 2022.","DOI":"10.1155\/2022\/1892123"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_088","unstructured":"https:\/\/www.allerin.com\/blog\/top-5-applications-of-deep-learning-in-healthcare."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_089","doi-asserted-by":"crossref","unstructured":"S. Keesara, A. Jonas, and K. Schulman, \u201cCovid-19 and health care\u2019s digital revolution,\u201d N. Engl. J. Med., vol. 382, no. 23. p. e82, 2020. 10.1056\/nejmp2005835.","DOI":"10.1056\/NEJMp2005835"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_090","doi-asserted-by":"crossref","unstructured":"S. Yang, P. Fichman, X. Zhu, M. Sanfilippo, S. Li, and K. R. Fleischmann, \u201cThe use of ICT during COVID\u201019,\u201d Proc. Assoc. Inf. Sci. Technol., vol. 57, p. e297, 2020. 10.1002\/pra2.297.","DOI":"10.1002\/pra2.297"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_091","doi-asserted-by":"crossref","unstructured":"A. Kapoor, S. Guha, M. K. Das, K. C. Goswami, and R. Yadav, \u201cDigital healthcare: The only solution for better healthcare during COVID-19 pandemic? Indian. Heart J., vol. 72, no. 2, pp. 61\u201364, 2020.","DOI":"10.1016\/j.ihj.2020.04.001"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_092","unstructured":"https:\/\/towardsdatascience.com\/medical-diagnosis-with-a-convolutional-neural-networkab0b6b455a20."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_093","doi-asserted-by":"crossref","unstructured":"D. R. Sarvamangala and R. V. Kulkarni, \u201cConvolutional neural networks in medical image understanding: a survey,\u201d Evol. Intel., vol. 15, no. 4, pp. 1\u201322, 2021. 10.1007\/s12065-020-00540-3.","DOI":"10.1007\/s12065-020-00540-3"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_094","doi-asserted-by":"crossref","unstructured":"S. S. Yadav and S. M. Jadhav, \u201cDeep convolutional neural network based medical image classification for disease diagnosis,\u201d J. Big Data, vol. 6, p. 113, 2019. 10.1186\/s40537-019-0276-2.","DOI":"10.1186\/s40537-019-0276-2"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_095","doi-asserted-by":"crossref","unstructured":"L. Wang and A. Wong, COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images, arXiv preprint arXiv:2003.09871, 2020.","DOI":"10.1038\/s41598-020-76550-z"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_096","unstructured":"R. M. Sadek, S. A. Mohammed, A. R. K. Abunbehan, A. K. H. A. Ghattas, M. R. Badawi, M. N. Mortaja, et al., \u201cParkinson\u2019s disease prediction using artificial neural network\u201d Int. J. Acad. Health Med. Res., vol. 3, pp. 1\u20138, 2019. http:\/\/ijeais.org\/wpcontent\/uploads\/2019\/01\/IJAHMR190101."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_097","doi-asserted-by":"crossref","unstructured":"Z. Soltani and A. Jafarian, \u201cA new artificial neural networks approach for diagnosing diabetes disease type II,\u201d Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 6, pp. 89\u201394, 2016.","DOI":"10.14569\/IJACSA.2016.070611"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_098","unstructured":"N. S. El_Jerjawi and S. S. Abu-Naser, \u201cDiabetes prediction using artificial neural network,\u201d Int. J. Adv. Sci. Technol., vol. 121, pp. 55\u201364, 2018."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_099","unstructured":"Y. Zhang, Z. Lin, Y. Kang, R. Ning, and Y. Meng, \u201cA feed-forward neural network model for the accurate prediction of diabetes mellitus,\u201d Int. J. Sci. Technol. Res., vol. 7, no. 8, pp. 151\u2013155, 2018, https:\/\/www.scopus.com\/inward\/record.uri? eid = 2-s2.085059910862&partnerID = 40&md5 = 40cdc4d37e47645feb76229e7b9c9dfd."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_100","doi-asserted-by":"crossref","unstructured":"M. Asad, U. Qamar, and M. Abbas, \u201cBlood glucose level prediction of diabetic type 1 patients using nonlinear autoregressive neural networks,\u201d J. Healthc. Eng., vol. 2021, p. 6611091, 7 pages, 2021. 10.1155\/2021\/6611091.","DOI":"10.1155\/2021\/6611091"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_101","doi-asserted-by":"crossref","unstructured":"S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, et al., \u201cHealthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments,\u201d Future Gener. Comput. Syst., vol. 104, pp. 187\u2013200, 2020.","DOI":"10.1016\/j.future.2019.10.043"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_102","doi-asserted-by":"crossref","unstructured":"F. Ali, S. El-Sappagh, S. M. R. Islam, D. Kwak, A. Ali, M. Imran, et al., \u201cA smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion,\u201d Inf. Fusion., vol. 63, pp. 208\u2013222, 2020. 10.1016\/j.inffus.2020.06.008.","DOI":"10.1016\/j.inffus.2020.06.008"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_103","doi-asserted-by":"crossref","unstructured":"S. N. Pasha, D. Ramesh, S. Mohmmad, and A. Harshavardhan, Cardiovascular disease prediction using deep learning techniques, In: IOP Conference Series: Materials Science and Engineering, Vol. 981. No. 2. IOP Publishing, 2020, December, p. 022006.","DOI":"10.1088\/1757-899X\/981\/2\/022006"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_104","doi-asserted-by":"crossref","unstructured":"H. Naz and S. Ahuja, \u201cDeep learning approach for diabetes prediction using PIMA Indian dataset,\u201d J. Diabetes Metab. Disord., vol. 19, no. 1, pp. 391\u2013403, 2020.","DOI":"10.1007\/s40200-020-00520-5"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_105","doi-asserted-by":"crossref","unstructured":"S. Shafqat, M. Fayyaz, H. A. Khattak, M. Bilal, S. Khan, O. Ishtiaq, et al., \u201cLeveraging deep learning for designing healthcare analytics heuristic for diagnostics,\u201d Neural Process. Lett., vol. 55, no. 1, pp. 53\u201379, 2021. 10.1007\/s11063021-10425-w.","DOI":"10.1007\/s11063-021-10425-w"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_106","doi-asserted-by":"crossref","unstructured":"P. Silva, E. Luz, G. Silva, G. Moreira, R. Silva, D. Lucio, et al., \u201cCOVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis,\u201d Inform. Med. Unlocked, vol. 20, no. 1, p. 100427, 2020. 10.1016\/j.imu.2020.100427.","DOI":"10.1016\/j.imu.2020.100427"},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_107","unstructured":"E. E. D. Hemdan, M. A. Shouman, and M. E. Karar, COVIDX-Net: a framework of deep learning classifiers to diagnose Covid-19 in x-ray images, arXiv preprint arXiv:2003.11055, 2020."},{"key":"2025073006061527313_j_pjbr-2022-0108_ref_108","doi-asserted-by":"crossref","unstructured":"A. Bhandary, G. A. Prabhu, V. Rajinikanth, K. P. Thanaraj, S. C. Satapathy, D. E. Robbins, et al., \u201cDeep-learning framework to detect lung abnormality \u2013 A study with chest X-Ray and lung CT scan images,\u201d Pattern Recognit. Lett., vol. 129, pp. 271\u2013278, 2019. 10.1016\/j.patrec.2019.11.013.","DOI":"10.1016\/j.patrec.2019.11.013"}],"container-title":["Paladyn, Journal of Behavioral Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0108\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:07:27Z","timestamp":1753855647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0108\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":108,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,7,29]]},"published-print":{"date-parts":[[2023,7,29]]}},"alternative-id":["10.1515\/pjbr-2022-0108"],"URL":"https:\/\/doi.org\/10.1515\/pjbr-2022-0108","relation":{},"ISSN":["2081-4836"],"issn-type":[{"type":"electronic","value":"2081-4836"}],"subject":[],"published":{"date-parts":[[2023,1,1]]},"article-number":"20220108"}}