Artificial Intelligence and Its Role in Healthcare in Increasing the Accuracy of Diagnosis
Abstract
AI is transforming the diagnosis sector by analysing huge quantities of medical information, like patient records, tests, and photographs, super-fast and extremely precise, unlike the more traditional means. Designed around patient outcomes, AI-powered systems may enable an earlier diagnosis of the disease, personalised approaches to the patients, and better distribution of resources to meet the patient needs. Tele-echocardiography with the help of AI has a huge potential and can be applied in more rural and distant parts of Japan where a shortage of doctors and other medical staff can cause issues with delivering patients with the advanced treatment they require. Recent changes in AI-based technologies have created new possibilities in optimization of echocardiographic procedure. Artificial intelligence algorithms can enhance the quality of pictures, automate the measurements tool, and even assist in diagnosing a heart problem. Among the lately advanced AI applications is the analysis of pictures; to give some examples, the accuracy level of deep learning when applied to identify and classify cardiac structures turns out to be up to 98%. These technology also allow automated measurements of parameters such as the volume of the ventricle, the ejection fraction and other significant parameters enhancing consistency and reducing human error in measurement. The AI enhances the efficiency and quality of remote administration of cardiac care, makes it more accessible, enables remote assessments in real-time and constant monitoring. To effectively implement AI in echocardiography, however, it is essential to alleviate some of the reservations about data privacy, openness, and incorporation into the clinical workflow, as well as of ethical concerns. As soon as those barriers are removed, AI will provide major changes to echocardiography, resulting in the improved cardiac health of all people in the future.
Full text article
References
Alex, T. J. and Calefato, F. F. dataset as a repository of biomedical information.
Ali, M. R., & Dey, N. Clinical Decision Support System.
Alzubaidi, M. A., Pardhan, S., Tighe, B., Balasubramanian, M., Subhi, Y., & Hernowo, A. T. Utilization of machine learning for the assessment of ocular and systemic clinical interests.
Amisha, P. M., Pathania, M., Rathaur, V. K. and Kowtkar, T. Overview of artificial intelligence in medicine.
Belhoula, M., Koifman, A., Cen, S., et al. An introduction to machine-learning algorithms for cardiovascular endpoint predictions using large-scale mitochondrial genome information through advancing precision medicine.
Bermant, L. G. Pathology consultation on breast reduction and mastopexy in patients with and without risks.
Bernardinus, E., Bouazza-Marouf, K., & Althoefer, K. Artificial intelligence in medical applications: An overview and taxonomy.
Boehler, C. & Collins, M. P. The Liverpool Lung Project risk prediction model.
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., ... & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689. springer.com
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 …. Diagnostic pathology. springer.com
Venigandla, K. (2022). Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare. Power System Technology. powertechjournal.com
Mirbabaie, M., Stieglitz, S., & Frick, N. R. J. (2021). Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health and Technology. springer.com
Tsikala Vafea, M., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E. K., Kalligeros, M., & Mylonakis, E. (2020). Emerging technologies for use in the study, diagnosis, and treatment of patients with COVID-19. Cellular and molecular bioengineering, 13, 249-257. springer.com
Gill, A. Y., Saeed, A., Rasool, S., Husnain, A., & Hussain, H. K. (2023). Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses-a Comprehensive Review of AI's Impact on Medical Diagnosis. Journal of World Science, 2(10), 1638-1652. rivierapublishing.id
Kalra, N., Verma, P., & Verma, S. (2024). Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques. Computers in Biology and Medicine. [HTML]
Shiwlani, A., Khan, M., Sherani, A. M. K., Qayyum, M. U., & Hussain, H. K. (2024). REVOLUTIONIZING HEALTHCARE: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PATIENT CARE, DIAGNOSIS, AND TREATMENT. JURIHUM: Jurnal Inovasi dan Humaniora, 1(5), 779-790. jurnalmahasiswa.com
Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., ... & Islam, S. R. (2020). Medical diagnostic systems using artificial intelligence (ai) algorithms: Principles and perspectives. IEEE Access, 8, 228049-228069. ieee.org
Hussain, S., Mubeen, I., Ullah, N., Shah, S. S. U. D., Khan, B. A., Zahoor, M., ... & Sultan, M. A. (2022). Modern diagnostic imaging technique applications and risk factors in the medical field: a review. BioMed research international, 2022(1), 5164970. wiley.com
Zeb, S., Nizamullah, F. N. U., Abbasi, N., & Fahad, M. (2024). AI in Healthcare: Revolutionizing Diagnosis and Therapy. International Journal of Multidisciplinary Sciences and Arts, 3(3), 118-128. itscience.org
Thomasian, N. M., Kamel, I. R., & Bai, H. X. (2022). Machine intelligence in non-invasive endocrine cancer diagnostics. Nature Reviews Endocrinology. nature.com
Doolub, G., Mamalakis, M., Alabed, S., Van der Geest, R. J., Swift, A. J., Rodrigues, J. C., ... & Dastidar, A. (2023). Artificial intelligence as a diagnostic tool in non-invasive imaging in the assessment of coronary artery disease. Medical Sciences, 11(1), 20. mdpi.com
Stamate, E., Piraianu, A. I., Ciobotaru, O. R., Crassas, R., Duca, O., Fulga, A., ... & Ciobotaru, O. C. (2024). Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years. Diagnostics, 14(11), 1103. mdpi.com
Kanan, M., Alharbi, H., Alotaibi, N., Almasuood, L., Aljoaid, S., Alharbi, T., ... & Mufti, A. (2024). AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis. Cancers, 16(3), 674. mdpi.com
Sun, M. & Cui, C. (2024). Advanced AI-driven image fusion techniques in lung cancer diagnostics: systematic review and meta-analysis for precisionmedicine. Robotic Intelligence and Automation. [HTML]
Gao, S., Xu, Z., Kang, W., Lv, X., Chu, N., Xu, S., & Hou, D. (2024). Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening. BMC Medical Imaging, 24. nih.gov
Wallace, W., Chan, C., Chidambaram, S., Hanna, L., Iqbal, F. M., Acharya, A., ... & Darzi, A. (2022). The diagnostic and triage accuracy of digital and online symptom checker tools: a systematic review. NPJ digital medicine, 5(1), 118. nature.com
Van den Bergh, P. Y. K. & van Doorn…, P. A. (2021). European Academy of Neurology/Peripheral Nerve Society guideline on diagnosis and treatment of chronic inflammatory demyelinating polyradiculoneuropathy …. Journal of the …. wiley.com
Swift, A., Heale, R., & Twycross, A. (2020). What are sensitivity and specificity?. Evidence-Based Nursing. archive.org
Pascoal, E., Wessels, J. M., Aas‐Eng, M. K., Abrao, M. S., Condous, G., Jurkovic, D., ... & Leonardi, M. (2022). Strengths and limitations of diagnostic tools for endometriosis and relevance in diagnostic test accuracy research. Ultrasound in obstetrics & gynecology, 60(3), 309-327. wiley.com
O'Neill, R. S. & Stoita, A. (2021). Biomarkers in the diagnosis of pancreatic cancer: Are we closer to finding the golden ticket?. World journal of gastroenterology. nih.gov
Newman-Toker, D. E., Wang, Z., Zhu, Y., Nassery, N., Tehrani, A. S. S., Schaffer, A. C., ... & Siegal, D. (2021). Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the “Big Three”. Diagnosis, 8(1), 67-84. degruyter.com
Schiff, G. D., Volodarskaya, M., Ruan, E., Lim, A., Wright, A., Singh, H., & Nieva, H. R. (2022). Characteristics of disease-specific and generic diagnostic pitfalls: a qualitative study. JAMA Network Open, 5(1), e2144531-e2144531. jamanetwork.com
Utrecht, S. (2023). The Future of Diagnosis: Navigating Uncertainty. Planning. degruyter.com
Krenitsky, N. M. & Goffman, D. (2024). Diagnostic Errors in Obstetric Morbidity and Mortality: Methods for and Challenges in Seeking Diagnostic Excellence. Journal of Clinical Medicine. nih.gov
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International journal of environmental research and public health, 18(1), 271. mdpi.com
Bohr, A. & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in healthcare. nih.gov
Edlow, J. A. & Pronovost, P. J. (2023). Misdiagnosis in the emergency department: time for a system solution. JAMA. [HTML]
Aluru, K. S. (2023). AI-Powered Diagnosis: Enhancing Accuracy and Efficiency in Healthcare. International Journal of Advanced Engineering Technologies and Innovations, 1(02), 466-489. ijaeti.com
Sayem, M. A., Taslima, N., Sidhu, G. S., Chowdhury, F., Sumi, S. M., Anwar, A. S., & Rowshon, M. (2023). AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices. Int. J. Recent Innov. Trends Comput. Commun, 11(10), 1109-1122. researchgate.net
Chen, X. (2024). AI in Healthcare: Revolutionizing Diagnosis and Treatment through Machine Learning. MZ Journal of Artificial Intelligence. mzjournal.com
Mei, X., Lee, H. C., Diao, K. Y., Huang, M., Lin, B., Liu, C., ... & Yang, Y. (2020). Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, 26(8), 1224-1228. nature.com
Kalita, A. J., Boruah, A., Das, T., Mazumder, N., Jaiswal, S. K., Zhuo, G. Y., ... & Kao, F. J. (2024). Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications. In Biomedical Imaging: Advances in Artificial Intelligence and Machine Learning (pp. 1-61). Singapore: Springer Nature Singapore. [HTML]
Nazarian, S., Glover, B., Ashrafian, H., Darzi, A., & Teare, J. (2021). Diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterization of colorectal polyps: systematic review and meta-analysis. Journal of medical Internet research, 23(7), e27370. jmir.org
Sharif, A. & Purdie, M. S. (). Bone Imaging and Segmentation: Rigid Bone Positioning with AI and Triple Shadow Check Method. researchgate.net. researchgate.net
Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics. springer.com
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, 106848. sciencedirect.com
Moore, C. M. (2022). The challenges of health inequities and AI. Intelligence-Based Medicine. sciencedirect.com
Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology, 12(1), 100702. [HTML]
Rossi, S., Antal, A., Bestmann, S., Bikson, M., Brewer, C., Brockmöller, J., ... & Hallett, M. (2021). Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clinical Neurophysiology, 132(1), 269-306. sciencedirect.com
Stahl, B. C. & Eke, D. (2024). The ethics of ChatGPT–Exploring the ethical issues of an emerging technology. International Journal of Information Management. sciencedirect.com
Tang, Y., Xiong, J., Becerril-Arreola, R., & Iyer, L. (2020). Ethics of blockchain: A framework of technology, applications, impacts, and research directions. Information Technology & People, 33(2), 602-632. researchgate.net
Thenault, R., Kaulanjan, K., Darde, T., Rioux-Leclercq, N., Bensalah, K., Mermier, M., ... & Mathieu, R. (2020). The application of artificial intelligence in prostate cancer management—What improvements can be expected? A systematic review. Applied Sciences, 10(18), 6428. mdpi.com
Giannini, V., Mazzetti, S., Defeudis, A., Stranieri, G., Calandri, M., Bollito, E., ... & Regge, D. (2021). A fully automatic artificial intelligence system able to detect and characterize prostate cancer using multiparametric MRI: multicenter and multi-scanner validation. Frontiers in Oncology, 11, 718155. frontiersin.org
Schmidt, B., Soerensen, S. J. C., Bhambhvani, H. P., Fan, R. E., Bhattacharya, I., Choi, M. H., ... & Sonn, G. A. (2024). External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens. BJU international. wiley.com
Almansouri, N. E., Awe, M., Rajavelu, S., Jahnavi, K., Shastry, R., Hasan, A., ... & Haider, A. (2024). Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus, 16(3). nih.gov
Salvi, M., Acharya, M. R., Seoni, S., Faust, O., Tan, R. S., Barua, P. D., ... & Acharya, U. R. (2024). Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(3), e1530. [HTML]
Boulif, A., Ananou, B., Ouladsine, M., & Delliaux, S. (2023). A literature review: ecg-based models for arrhythmia diagnosis using artificial intelligence techniques. Bioinformatics and Biology Insights, 17, 11779322221149600. sagepub.com
Rivera, S. C., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., Ashrafian, H., ... & Yau, C. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. The Lancet Digital Health, 2(10), e549-e560. thelancet.com
Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet. Journal of medical Internet research, 22(9), e22845. jmir.org
Quazi, S. (2022). Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology. springer.com
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science. springer.com
Tapeh, A. T. G., & Naser, M. Z. (2023). Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 30(1), 115-159. researchgate.net
Senbekov, M., Saliev, T., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Aitenova, N., ... & Fakhradiyev, I. (2020). The recent progress and applications of digital technologies in healthcare: a review. International journal of telemedicine and applications, 2020(1), 8830200. wiley.com
Afzal, A. (2020). Molecular diagnostic technologies for COVID-19: Limitations and challenges. Journal of advanced research. sciencedirect.com
Tariq, M., Hayat, Y., Hussain, A., Tariq, A., & Rasool, S. (2020). Principles and Perspectives in Medical Diagnostic Systems Employing Artificial Intelligence (AI) Algorithms. International Research Journal of Economics and Management Studies IRJEMS, 3(1). irjems.org
Gastounioti, A., Desai, S., Ahluwalia, V. S., & Conant…, E. F. (2022). Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer …. springer.com
Panahiazar, M., Chen, N., Lituiev, D., & Hadley, D. (2022). Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases. Clinical & experimental metastasis, 39(1), 249-254. springer.com
Penn-Nicholson, A., Gomathi, S. N., Ugarte-Gil, C., Meaza, A., Lavu, E., Patel, P., ... & Truenat Trial Consortium. (2021). A prospective multicentre diagnostic accuracy study for the Truenat tuberculosis assays. European Respiratory Journal, 58(5). ersnet.org
Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12-30. sciencedirect.com
Subbiah, V. (2023). The next generation of evidence-based medicine. Nature medicine. nature.com
Adler-Milstein, J., Aggarwal, N., Ahmed, M., Castner, J., Evans, B. J., Gonzalez, A. A., ... & Williams, A. (2022). Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis. NAM perspectives, 2022. nih.gov
Authors

This work is licensed under a Creative Commons Attribution 4.0 International License.