Transforming Healthcare: Harnessing the Power of AI in the Modern Era
DOI:
10.47709/ijmdsa.v2i1.2513Keywords:
Artificial intelligence, healthcare providers, collaboration, partnerships, diagnostics, treatment, personalized medicine, clinical decision support systems, process optimization, patient engagement.Dimension Badge Record
Abstract
Patient care and medical research are changing as a result of the application of artificial intelligence (AI) in healthcare. To fully realize the potential of AI technology, collaborative relationships between AI developers and healthcare providers are essential. This study examines the advantages and prospects of collaborating with healthcare professionals to improve healthcare outcomes. Personalized medicine, clinical decision support systems, healthcare process optimization, patient engagement, and ethical considerations are just a few of the areas where AI and healthcare practitioners are collaborating. Significant progress can be made by fusing the knowledge of healthcare professionals with AI's powers in data analysis, pattern recognition, and predictive modeling. Advancements in diagnosis and therapy are a major area of collaboration. Healthcare practitioners can gain from enhanced diagnostic precision, early illness identification, and exact treatment planning by integrating AI algorithms with patient data. Enhanced patient outcomes and improved healthcare delivery are the outcomes. The development of personalized medicine techniques is also made possible by collaboration. Healthcare professionals can customize treatment strategies based on unique genetic markers, biomarkers, and clinical factors by utilizing AI algorithms to examine patient data. This collective effort results in improved treatments and treatment outcomes. Clinical decision support system development is facilitated by collaborations between AI and healthcare professionals. By analyzing patient data, medical literature, and clinical recommendations using AI technology, these systems offer real-time guidance to medical personnel. Clinical decision support systems increase the effectiveness of diagnosis, the choice of treatment, and patient safety by strengthening decision-making abilities. In healthcare settings, collaboration also emphasizes process improvement, increasing effectiveness, and resource management. Artificial intelligence (AI) algorithms can examine operational data and patient flow patterns to spot inefficiencies, resulting in the simplification of administrative work, enhanced patient scheduling, and better resource management. Costs are reduced, operational effectiveness is raised, and patient experiences are improved as a result. Patient participation and experience are another facet of partnership. Artificial intelligence-enabled virtual assistants and catboats offer individualized support, respond to patient questions, and deliver health information. These resources improve patient satisfaction, ease of access to healthcare, and patient empowerment in health management. AI and healthcare practitioners working together must take ethical issues and legal compliance very seriously. It is crucial to protect patient privacy, guarantee data security, and abide by ethical standards and regulatory frameworks. Collaborations can improve healthcare results and preserve patient trust by taking these factors into account. AI and healthcare providers working together could change how patients are treated, promote medical research, and enhance patient outcomes. Partnerships that make use of AI technologies and integrate them with healthcare knowledge promote innovation, improve patient engagement, optimize diagnostic and therapeutic procedures, and ensure ethical and legal compliance. AI and healthcare professionals work together continuously to enhance patient outcomes and the standard of care, shaping the future of healthcare delivery.
Abstract viewed = 6146 times
References
Wardat, Y., Hidayat, R., Belbase, S., & Al Farra, N. A comparison of Mathematical Modeling Attitude between Malaysian and United Arab Emirates (UAE) Teachers. Frontiers in Psychology, 14, 1054.
Rashid, M. T., Hossain, A., & Hussain, M. N. (2017, November). Automated traffic measurement system based on FCD and image processing. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 1381-1385). IEEE.
Wardat, Y., Belbase, S., Tairab, H., Takriti, R. A., Efstratopoulou, M., & Dodeen, H. (2022). The influence of school factors on students’ mathematics achievements in trends in international mathematics and science study (TIMSS) in Abu Dhabi Emirate schools. Education Sciences, 12(7), 424.
Kumar, A., Singh, S., & Al-Bahrani, M. (2022). Enhancement in power conversion efficiency and stability of perovskite solar cell by reducing trap states using trichloroacetic acid additive in anti-solvent. Surfaces and Interfaces, 34, 102341.
Rashid, M. T., Abir, I. K., Shourove, N. S., Muntaha, R., & Rhaman, M. K. (2016, May). Intelligent intrusion prevention system for households based on system-on-chip computer. In 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-5). IEEE.
AlAli, R., Wardat, Y., & Al-Qahtani, M. (2023). SWOM strategy and influence of its using on developing mathematical thinking skills and on metacognitive thinking among gifted tenth-grade students. EURASIA Journal of Mathematics, Science and Technology Education, 19(3), em2238.
Siddiq, M. (2022). Revolutionizing Drug Discovery; Transformative Role of Machine Learning. BULLET: Jurnal Multidisiplin Ilmu, 1(02), 162-170.
Stoica, G., & Wardat, Y. (2021). An Inequality Can Change Everything... Am. Math. Mon., 128(9), 810.
Zhang, Y., Zong, R., Shang, L., Rashid, M. T., & Wang, D. (2021, June). Superclass: A deep duo-task learning approach to improving qos in image-driven smart urban sensing applications. In 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) (pp. 1-6). IEEE.
Rashid, M. T., Zhang, D., & Wang, D. (2020, July). A Computational Model-Driven Hybrid Social Media and Drone-Based Wildfire Monitoring Framework. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1362-1363). IEEE.
Mohammad, A., Mahjabeen, F., Tamzeed-Al-Alam, M., Bahadur, S., & Das, R. (2022). Photovoltaic Power plants: A Possible Solution for Growing Energy Needs of Remote Bangladesh. NeuroQuantology, 20(16), 1164.
Chowdhury, M. S. S., Nawal, M. F., Rashid, T., & Rhaman, K. (2015, December). Terminal analysis of the operations of a Rescue Robot constructed for assisting secondary disaster situations. In 2015 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-5). IEEE.
Rashid, M. T., Zhang, D. Y., & Wang, D. (2019, December). Socialcar: A task allocation framework for social media driven vehicular network sensing systems. In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) (pp. 125-130). IEEE.
AlArabi, K., Tairab, H., Wardat, Y., Belbase, S., & Alabidi, S. (2022). ENHANCING THE LEARNING OF NEWTON’S SECOND LAW OF MOTION USING COMPUTER SIMULATIONS. Journal of Baltic Science Education, 21(6).
Rashid, M. T., Chowdhury, P., & Rhaman, M. K. (2015, December). Espionage: A voice guided surveillance robot with DTMF control and web based control. In 2015 18th International Conference on Computer and Information Technology (ICCIT) (pp. 419-422). IEEE.
Yasar, M. S., & Rashid, M. (2015). Implementation of dynamic traffic light controllers using artificial neural networks to diminish traffic ordeals. In IEEE European Modelling Symposium.
Rashid, M. T., Hossain, A., & Hussain, M. N. (2017, November). Automated traffic measurement system based on FCD and image processing. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 1381-1385). IEEE.
Shams, A. T. Ecologized Humanity Versus Humanized Ecology: Critical Approach to Camera Narratives in Cinematic Arts.
Siddiq, M. (2020). ML-based Medical Image Analysis for Anomaly Detection in CT Scans, X-rays, and MRIs. Devotion Journal of Community Service, 2(1), 53-64.
Rashid, M. T., Wei, N., & Wang, D. (2021). A survey on social-physical sensing. arXiv preprint arXiv:2104.01360.
Hussain, H. K., Ahmad, A., Adam, M. A., Kiruthiga, T., & Gupta, K. (2023, February). Prediction of Blood Lactate Levels in Children after Cardiac Surgery using Machine Learning Algorithms. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1163-1169). IEEE.
Alneyadi, S., Abulibdeh, E., & Wardat, Y. (2023). The Impact of Digital Environment vs. Traditional Method on Literacy Skills; Reading and Writing of Emirati Fourth Graders. Sustainability, 15(4), 3418.
Siddiq, M. (2022). Use of Machine Learning to predict patient developing a disease or condition for early diagnose. International Journal of Multidisciplinary Sciences and Arts, 1(1).
Shams, A. T., & Abir, A. A. A Comparative Study of Dehumanizing Gender Narrative in D. H Lawrence's Sons and Lovers and Franz Kafka's The Metamorphosis.
Rashid, M. T., & Wang, D. (2021, October). Unravel: An anomalistic crowd investigation framework using social airborne sensing. In 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) (pp. 1-10). IEEE.
Siddiq, M. (2021). Integration of Machine Learning in Clinical Decision Support Systems. Eduvest-Journal of Universal Studies, 1(12), 1579-1591.
Gupta, K. (2023, February). The Intelligent Heart Rate Monitoring Model for Survivability Prediction of Cardiac Arrest Patients Using Deep Cardiac Learning Model. In 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) (pp. 376-381). IEEE
Rashid, M. T., & Mahmud, S. A. (2017, October). Intelligent seeker robot to locate dead bodies and survivors under debris. In 2017 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1-6). IEEE.
Bansal, T., Gunasekaran, K., Wang, T., Munkhdalai, T., & McCallum, A. (2021). Diverse distributions of self-supervised tasks for meta-learning in NLP. arXiv preprint arXiv:2111.01322.
Hussain, H. K., Tariq, A., & Gill, A. Y. (2023). Role of AI in Cardiovascular Health Care; a Brief Overview. Journal of World Science, 2(4), 794-802..
Shams, A. T. Musical Translation Approach (MTA) for Cognitive Learning in Translation Studies Pedagogy in Bangladesh.
Alarabi, K., & Wardat, Y. (2021). UAE-based teachers’ hindsight judgments on physics education during the COVID-19 pandemic. Psychology and Education Journal, 58(3), 2497-2511.
Rashid, M. T., Zhang, D. Y., & Wang, D. (2021, July). HeteroSAS: A Heterogeneous Resource Management Framework for" All-in-the-Air" Social Airborne Sensing in Disaster Response. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 132-139). IEEE.
Abir, I. K., Rashid, M. T., Zaman, H., Hassan, M., Rafique, R., & Rhaman, M. K. (2015, November). Development and feasibility evaluation of a low cost smart voyage data recorder: Marine Black Box. In 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 347-352). IEEE.
Yeruva, A. R., Durga, C. V., Gokulavasan, B., Pant, K., Chaturvedi, P., & Srivastava, A. P. (2022, October). A smart healthcare monitoring system based on fog computing architecture. In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 904-909). IEEE.
Shams, A. T., & Akter, S. (2022). Eco-Centric Versus Anthropocentric Approach in Literary Pedagogy: Inclusion of Non-Human Narratives as Teaching Social Justice.
Rashid, M. T., Zhang, D., & Wang, D. (2020, July). Demo abstract: Social media-driven uav sensing frameworks in disaster response applications. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1340-1341). IEEE.
Sujatha, N., Valli, L. N., Prema, A., Rathiha, S. K., & Raja, V. (2022). Initial centroid selection for K-means clustering algorithm using the statistical method.
Balamurugan, R. J., AL-bonsrulah, H. A., Raja, V., Kumar, L., Kannan, S. D., Madasamy, S. K., ... & Al-Bahrani, M. (2022). Design and Multiperspectivity based performance investigations of H-Darrieus vertical Axis wind turbine through computational fluid dynamics adopted with moving reference frame approaches. International Journal of Low-Carbon Technologies.
Rashid, M. T., Zhang, D. Y., & Wang, D. (2020). DASC: Towards a road Damage-Aware Social-media-driven Car sensing framework for disaster response applications. Pervasive and Mobile Computing, 67, 101207.
Al-Bahrani, M., Bouaissi, A., & Cree, A. (2022). The fabrication and testing of a self-sensing MWCNT nanocomposite sensor for oil leak detection. International Journal of Low-Carbon Technologies, 17, 622-629.
Akhter, A., & Shams, A. T. (2022). Identity Economics in Emily Brontë’s Wuthering Heights: An Empathetic Inquiry into Psychoanalysis. SCHOLARS: Journal of Arts & Humanities, 4(2), 74-80.
Mohseni, S., Yang, F., Pentyala, S., Du, M., Liu, Y., Lupfer, N., ... & Ragan, E. (2021, May). Machine learning explanations to prevent overtrust in fake news detection. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 421-431).
Rashid, M. T., & Wang, D. (2022). SEIS: A spatiotemporal-aware event investigation framework for social airborne sensing in disaster recovery applications. Pervasive and Mobile Computing, 87, 101717.
Tashtoush, M. A., Wardat, Y., Aloufi, F., & Taani, O. (2022). The Effectiveness of Teaching Method Based on the Components of Concept-Rich Instruction Approach in Students Achievement on Linear Algebra Course and Their Attitudes Towards Mathematics. Journal of Higher Education Theory & Practice, 22(7).
Rashid, M. T., Wei, N., & Wang, D. (2021). A survey on social-physical sensing. arXiv preprint arXiv:2104.01360.
Al-Bahrani, M. (2019). The Manufacture and Testing of Self-Sensing CNTs Nanocomposites for Damage Detecting Applications (Doctoral dissertation, University of Plymouth).
Pentyala, S., Liu, M., & Dreyer, M. (2019). Multi-task networks with universe, group, and task feature learning. arXiv preprint arXiv:1907.01791.
Siddiq, M. (2023). Exploring the Role of Machine Learning in Contact Tracing for Public Health: Benefits, Challenges, and Ethical Considerations. American Journal of Economic and Management Business (AJEMB), 2(4), 89-103.
Linder, R., Mohseni, S., Yang, F., Pentyala, S. K., Ragan, E. D., & Hu, X. B. (2021). How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking. Applied AI Letters, 2(4), e49.
Zahmatkesh, S., Rezakhani, Y., Arabi, A., Hasan, M., Ahmad, Z., Wang, C., & Ghodrati, I. (2022). An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB. Chemosphere, 308, 136304.
Downloads
ARTICLE Published HISTORY
Accepted Date: 2023-07-10
Published Date: 2023-07-10
Issue
Section
License
Copyright (c) 2023 Sanjay Patil, Harish Shankar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
