Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses - a Comprehensive Review of AI's Impact on Medical Diagnosis
DOI:
https://doi.org/10.58344/jws.v2i10.449Keywords:
algorithmic bias, case studies, data privacy, interpretabilityAbstract
The integration of machine learning into healthcare heralds a new era where the convergence of technology and human compassion reshapes the very essence of healing. This monumental shift transcends mere technological advancement; it represents a profound evolution in patient care. By unraveling intricate patterns within medical data, machine learning empowers healthcare professionals with early disease detection and precise risk assessment, augmenting human intuition rather than replacing it. This synergy between AI-driven insights and human expertise has led to remarkable achievements, from redefining radiological interpretations to foreseeing infectious disease outbreaks, painting a future where healthcare is not only precise but profoundly patient-centered. Yet, amidst these groundbreaking advancements, ethical considerations stand as pillars guiding responsible innovation. Upholding patient autonomy, ensuring data privacy, and addressing algorithmic bias are essential to maintain trust and integrity. As we navigate this transformative path, the promise of a healthcare landscape where healing becomes a symphony of technology and tradition becomes evident. It is a future where the well-being and hopes of millions are at the core, promising a brighter, more compassionate tomorrow for healthcare, where every diagnosis, treatment, and act of care resonates with the harmony of human expertise and technological marvels.
References
Ahmad, A., Tariq, A., Hussain, H. K., & Gill, A. Y. (2023). Equity and Artificial Intelligence in Surgical Care: A Comprehensive Review of Current Challenges and Promising Solutions. BULLET: Jurnal Multidisiplin Ilmu, 2(2), 443–455.
Ahmad, Z., Rahim, S., Zubair, M., & Abdul-Ghafar, J. (2021). Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosoph. Diagnostic Pathology, 16, 1–16.
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010.
Akhtar, N., Rahman, S., Sadia, H., & Perwej, Y. (2021). A holistic analysis of Medical Internet of Things (MIoT). Journal of Information and Computational Science, 11(4), 209–222.
Alhaidry, H. M., Fatani, B., Alrayes, J. O., Almana, A. M., Alfhaed, N. K., Alhaidry, H., Alrayes, J., Almana, A., & Alfhaed Sr, N. K. (2023). ChatGPT in Dentistry: A Comprehensive Review. Cureus, 15(4).
Bhati, N. S., Sharma, P., & Shakeel, H. (2023). Role of Internet of Things, artificial intelligence, and machine learning in biomedical devices: a comprehensive review. Internet of Things in Biomedical Sciences: Challenges and Applications, 1–9.
Carrillo?Perez, F., Pecho, O. E., Morales, J. C., Paravina, R. D., Della Bona, A., Ghinea, R., Pulgar, R., Pérez, M. del M., & Herrera, L. J. (2022). Applications of artificial intelligence in dentistry: A comprehensive review. Journal of Esthetic and Restorative Dentistry, 34(1), 259–280.
Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. Ieee Access, 8, 90225–90265.
Dash, S. S., Tiwari, S., & Nahak, K. (2023). REVOLUTIONIZING CARDIOVASCULAR DISEASE PREVENTION WITH MACHINE LEARNING: A COMPREHENSIVE REVIEW. Journal of Data Acquisition and Processing, 38(2), 2429.
Devi, K. J., Alghamdi, W., Divya, N., Alkhayyat, A., Sayyora, A., & Sathish, T. (2023). Artificial Intelligence in Healthcare: Diagnosis, Treatment, and Prediction. E3S Web of Conferences, 399, 4043.
Dhar, T., Dey, N., Borra, S., & Sherratt, R. S. (2023). Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust. IEEE Transactions on Technology and Society, 4(1), 68–75.
Herath, H., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076.
Hussain, M., Koundal, D., & Manhas, J. (2023). Deep learning-based diagnosis of disc degenerative diseases using MRI: a comprehensive review. Computers and Electrical Engineering, 105, 108524.
Iqbal, M. J., Javed, Z., Sadia, H., Qureshi, I. A., Irshad, A., Ahmed, R., Malik, K., Raza, S., Abbas, A., & Pezzani, R. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell International, 21(1), 1–11.
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93.
Karim, I., Tang, A., Raghavan, A., Martinez, C., Shimizu-Jozi, A., Jim, D., Dasgupta, R., & Abichandani, R. (n.d.). A Comprehensive Review of the Efficacy of Various Machine Learning Algorithms on the Diagnosis of Psychiatric Disorders.
Khezr, S., Moniruzzaman, M., Yassine, A., & Benlamri, R. (2019). Blockchain technology in healthcare: A comprehensive review and directions for future research. Applied Sciences, 9(9), 1736.
KIRBOGA, K. K., KUCUKSILLE, E. U., & Utku, K. (2021). The Impact of Artificial Intelligence on the Medical Area: Detailed Review. Journal of Multidisciplinary Developments, 6(1), 54–73.
Kooli, C., & Al Muftah, H. (2022). Artificial intelligence in healthcare: a comprehensive review of its ethical concerns. Technological Sustainability, 1(2), 121–131.
Kumar, P., Kumar, R., & Gupta, M. (2021). Deep learning based analysis of ophthalmology: A systematic review. EAI Endorsed Transactions on Pervasive Health and Technology, 7(29).
Lata, K., & Cenkeramaddi, L. R. (2023). Deep Learning for Medical Image Cryptography: A Comprehensive Review. Applied Sciences, 13(14), 8295.
Mall, P. K., Singh, P. K., Srivastav, S., Narayan, V., Paprzycki, M., Jaworska, T., & Ganzha, M. (2023). A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, 100216.
Motwani, A., Shukla, P. K., & Pawar, M. (2022). Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review. Artificial Intelligence in Medicine, 102431.
Pal, P., & Taqi, S. A. A. (2020). Advancements in Data Mining and Machine Learning Techniques for Predicting Human Diseases: A Comprehensive Review. International Journal of Research in Informative Science Application & Techniques (IJRISAT), 4(11), 19–35.
Parasar, D., Ali, A., Pillai, N. M., Shahi, A., Alfurhood, B. S., & Pant, K. (2023). Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 682–685.
Poalelungi, D. G., Musat, C. L., Fulga, A., Neagu, M., Neagu, A. I., Piraianu, A. I., & Fulga, I. (2023). Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. Journal of Personalized Medicine, 13(8), 1214.
Saeed, U., Shah, S. Y., Ahmad, J., Imran, M. A., Abbasi, Q. H., & Shah, S. A. (2022). Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. Journal of Pharmaceutical Analysis, 12(2), 193–204.
Samarpita, S., & Satpathy, R. N. (2022). Applications of Machine Learning in Healthcare: An Overview. 2022 1st IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA), 51–56.
Santosh, K. C., Gaur, L., Santosh, K. C., & Gaur, L. (2021). Introduction to ai in public health. Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact, 1–10.
Saqib, M., Iftikhar, M., Neha, F., Karishma, F., & Mumtaz, H. (2023). Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Frontiers in Medicine, 10, 1176192.
Shah, S. N. A., & Parveen, R. (2023). An Extensive Review on Lung Cancer Diagnosis Using Machine Learning Techniques on Radiological Data: State-of-the-art and Perspectives. Archives of Computational Methods in Engineering, 1–14.
Shaheen, M. Y. (2021). Applications of Artificial Intelligence (AI) in healthcare: A review. ScienceOpen Preprints.
Spann, A., Yasodhara, A., Kang, J., Watt, K., Wang, B. O., Goldenberg, A., & Bhat, M. (2020). Applying machine learning in liver disease and transplantation: a comprehensive review. Hepatology, 71(3), 1093–1105.
Tassew, T., & Nie, X. (2022). A Comprehensive Review of the Application of Machine Learning in Medicine and Health Care.
Velichko, Y. S., Gennaro, N., Karri, M., Antalek, M., & Bagci, U. (2023). A Comprehensive Review of Deep Learning Approaches for Magnetic Resonance Imaging Liver Tumor Analysis. Advances in Clinical Radiology, 5(1), 1–15.
Xiao, D., Meyers, P., Upperman, J. S., & Robinson, J. R. (2023). Revolutionizing Healthcare with ChatGPT: An Early Exploration of an AI Language Model’s Impact on Medicine at Large and its Role in Pediatric Surgery. Journal of Pediatric Surgery.
Yagi, M., Yamanouchi, K., Fujita, N., Funao, H., & Ebata, S. (2023). Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning. Journal of Clinical Medicine, 12(13), 4188.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of World Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
