The Power of AI-Assisted Diagnosis
DOI:
https://doi.org/10.4108/eetel.3772Keywords:
AI, Diagnosis, Efficiency, ChallengesAbstract
The rapid advancements in artificial intelligence (AI) have unleashed a wave of transformative technologies, and one area that has witnessed significant progress is AI-assisted diagnosis in healthcare. With the ability to analyze vast amounts of medical data, learn from patterns, and make accurate predictions, AI systems hold immense potential to revolutionize the diagnostic process, enabling earlier detection, improved accuracy, and personalized treatment recommendations. This review aims to explore the impact of AI in healthcare, specifically focusing on its role in assisting physicians with diagnosis, highlighting the benefits, challenges, and ethical considerations associated with the integration of AI systems into clinical practice. Through the utilization of AI's capabilities, the enhancement of patient outcomes, optimization of resource allocation, and the reshaping of medical professionals' approaches to diagnosis and treatment can be achieved.
References
Almotairi, K.H., et al., Impact of Artificial Intelligence on COVID-19 Pandemic: A Survey of Image Processing, Tracking of Disease, Prediction of Outcomes, and Computational Medicine. Big Data and Cognitive Computing, 2023. 7(1): p. 11.
Wang, J., S. Wang, and Y. Zhang, Artificial intelligence for visually impaired. Displays, 2023. 77: p. 102391.
Gorriz, J., et al., A hypothesis-driven method based on machine learning for neuroimaging data analysis. Neurocomputing, 2022. 510: p. 159-171.
Dicuonzo, G., et al., Healthcare system: Moving forward with artificial intelligence. Technovation, 2023. 120: p. 102510.
Philip, A.K., et al., Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors. Life, 2023. 13(1): p. 24.
Mourtzis, D., et al., A smart IoT platform for oncology patient diagnosis based on ai: towards the human digital twin. Procedia CIRP, 2021. 104: p. 1686-1691.
Díaz-Rodríguez, N., et al., Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 2023. 99: p. 101896.
Schwitzgebel, E., AI systems must not confuse users about their sentience or moral status. Patterns, 2023. 4(8): p. 100818.
Wang, S., Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Information Fusion, 2021. 76: p. 376-421.
Heyn, H.-M., E. Knauss, and P. Pelliccione, A compositional approach to creating architecture frameworks with an application to distributed AI systems. Journal of Systems and Software, 2023. 198: p. 111604.
Lee, C. and K. Cha, FAT-CAT—Explainability and augmentation for an AI system: A case study on AI recruitment-system adoption. International Journal of Human-Computer Studies, 2023. 171: p. 102976.
Zhang, Y.-D., Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Advances in Mechanical Engineering, 2016. 8(2).
Din, M., et al., Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. Journal of NeuroInterventional Surgery, 2023. 15(3): p. 262-271.
Wang, S., Detection of Dendritic Spines using Wavelet Packet Entropy and Fuzzy Support Vector Machine. CNS & Neurological Disorders - Drug Targets, 2017. 16(2): p. 116-121.
Chen, Z., et al., Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges. Journal of Digital Imaging, 2023. 36(1): p. 204-230.
Amiri, Z., et al., The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors. Sustainability, 2023. 15(16): p. 12406.
Chaddad, A., et al., Explainable, domain-adaptive, and federated artificial intelligence in medicine. IEEE/CAA Journal of Automatica Sinica, 2023. 10(4): p. 859-876.
Gala, D. and A.N. Makaryus, The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4. International Journal of Environmental Research and Public Health, 2023. 20(15): p. 6438.
Wang, S.-H. and S. Fernandes, AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM. IEEE Sensors Journal, 2022. 22(18): p. 17431 - 17438.
Prabhakar, B., R.K. Singh, and K.S. Yadav, Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device. Computerized Medical Imaging and Graphics, 2021. 87: p. 101818.
Plantec, Q., et al., Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry. Technovation, 2023. 124: p. 102763.
Cabitza, F., et al., Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis. Artificial Intelligence in Medicine, 2023. 138: p. 102506.
Ibrahim, M.S. and S. Saber, Machine Learning and Predictive Analytics: Advancing Disease Prevention in Healthcare. Journal of Contemporary Healthcare Analytics, 2023. 7(1): p. 53-71.
Zhang, Y., Deep learning in food category recognition. Information Fusion, 2023. 98: p. 101859.
Neri, L., et al., Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. Sensors, 2023. 23(10): p. 4805.
Chatterjee, S., S. Khorana, and H. Kizgin, Harnessing the potential of artificial intelligence to foster citizens’ satisfaction: An empirical study on India. Government information quarterly, 2022. 39(4): p. 101621.
Bhattamisra, S.K., et al., Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing, 2023. 7(1): p. 10.
Almansour, N.M., Triple-negative breast cancer: a brief review about epidemiology, risk factors, signaling pathways, treatment and role of artificial intelligence. Frontiers in Molecular Biosciences, 2022. 9: p. 836417.
Santhanam, P., et al., Artificial intelligence and body composition. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2023. 17(3): p. 102732.
Jiang, X., Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers, 2023. 15: p. 3608.
Gellert, G.A., et al., The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review. Telemedicine Reports, 2023. 4(1): p. 180-191.
Chaddad, A., et al., Survey of explainable AI techniques in healthcare. Sensors, 2023. 23(2): p. 634.
Ahuja, A.S., et al., The digital metaverse: Applications in artificial intelligence, medical education, and integrative health. Integrative Medicine Research, 2023. 12(1): p. 100917.
Jiang, X., A Survey on Artificial Intelligence in Posture Recognition. Comput Model Eng Sci, 2023. 137(1): p. 35-82.
Al-Antari, M.A., Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology! 2023, MDPI. p. 688.
Katzman, B.D., et al., Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagnostic and Interventional Imaging, 2023. 104(1): p. 6-10.
Binhowemel, S., et al., Role of Artificial Intelligence in Diabetes Research Diagnosis and Prognosis: A Narrative Review. Journal of Health Informatics in Developing Countries, 2023. 17(02): p. 1-15.
Whitehead, M., et al., Making the invisible visible: what can we do about biased AI in medical devices? British Medical Journal, 2023. 382: p. 44-58.
Bowles, J., D. Clifford, and J. Mohan, The place of charity in a public health service: inequality and persistence in charitable support for NHS Trusts in England. Social Science & Medicine, 2023. 322: p. 115805.
Wang, Y.-H. and G.-Y. Lin, Exploring AI-healthcare innovation: natural language processing-based patents analysis for technology-driven roadmapping. Kybernetes, 2023. 52(4): p. 1173-1189.
Ellahham, S. and N. Ellahham, Use of artificial intelligence for improving patient flow and healthcare delivery. J. Comput. Sci. Syst. Biol, 2019. 12(3).
Zhang, Y., A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm. Mathematical Problems in Engineering, 2013. 2013.
Zhang, Y., Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Journal of Experimental & Theoretical Artificial Intelligence, 2017. 29(2): p. 299-312.
Akila, S.M., E. Imanov, and K. Almezhghwi, Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia. Diagnostics, 2023. 13(13): p. 2199.
Hassani, H. and E.S. Silva, The role of ChatGPT in data science: how ai-assisted conversational interfaces are revolutionizing the field. Big data and cognitive computing, 2023. 7(2): p. 62.
Mohammad Amini, M., et al., Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate. Machine Learning and Knowledge Extraction, 2023. 5(3): p. 1023-1035.
Iman, M., H.R. Arabnia, and K. Rasheed, A review of deep transfer learning and recent advancements. Technologies, 2023. 11(2): p. 40.
Kebaili, A., J. Lapuyade-Lahorgue, and S. Ruan, Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. Journal of Imaging, 2023. 9(4): p. 81.
Grimmelikhuijsen, S., Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision‐making. Public Administration Review, 2023. 83(2): p. 241-262.
Harry, A., The Future of Medicine: Harnessing the Power of AI for Revolutionizing Healthcare. International Journal of Multidisciplinary Sciences and Arts, 2023. 2(1): p. 36-47.
Han, X., A survey on deep learning in COVID-19 diagnosis. Journal of Imaging, 2023. 9(1): p. 1.
Dhar, T., et al., Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust. IEEE Transactions on Technology and Society, 2023. 4(1): p. 68-75.
Ahmad, A., et al., Equity and Artificial Intelligence in Surgical Care: A Comprehensive Review of Current Challenges and Promising Solutions. BULLET: Jurnal Multidisiplin Ilmu, 2023. 2(2): p. 443-455.
Moreno-Sánchez, P.A., Data-Driven Early Diagnosis of Chronic Kidney Disease: Development and Evaluation of an Explainable AI Model. IEEE Access, 2023. 11: p. 38359-38369.
Satapathy, P., et al., Artificial intelligence in surgical education and training: opportunities, challenges, and ethical considerations–correspondence. International Journal of Surgery (London, England), 2023. 109(5): p. 1543.
Yu, K.-H., A.L. Beam, and I.S. Kohane, Artificial intelligence in healthcare. Nature biomedical engineering, 2018. 2(10): p. 719-731.
Ahmed, M.U., S. Barua, and S. Begum, Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—Case Studies. Signal Processing Techniques for Computational Health Informatics, 2021. 15: p. 261-291.
Kondylakis, H., et al., Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects. European Radiology Experimental, 2023. 7(1): p. 20.
Mijwil, M. and M. Aljanabi, Towards artificial intelligence-based cybersecurity: the practices and ChatGPT generated ways to combat cybercrime. Iraqi Journal For Computer Science and Mathematics, 2023. 4(1): p. 65-70.
Al Kuwaiti, A., et al., A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine, 2023. 13(6): p. 951.
Khattak, W.A. and F. Rabbi, Ethical Considerations and Challenges in the Deployment of Natural Language Processing Systems in Healthcare. International Journal of Applied Health Care Analytics, 2023. 8(5): p. 17-36.
Bharadiya, J.P., Machine Learning and AI in Business Intelligence: Trends and Opportunities. International Journal of Computer (IJC). 48(1): p. 123-134.
Chaudhry, I.S., et al., Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT—A Case Study. Cogent Education, 2023. 10(1): p. 2210461.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on e-Learning
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
