Author(s)

Liam Nguyen

Affiliation(s)

School of Computing and Information Systems, University of Melbourne, Australia.

Corresponding Author

Liam Nguyen

ABSTRACT

Deep learning, a rapidly advancing subfield of artificial intelligence, has emerged as a transformative technology poised to revolutionize the field of medical diagnosis. This comprehensive review article provides a thorough examination of the current state-of-the-art applications of deep learning in disease detection and classification, highlighting key breakthroughs, challenges, and future prospects that hold immense promise for the future of healthcare. By automating the extraction of complex patterns from diverse medical data sources, deep learning algorithms have demonstrated the capacity to enhance the accuracy, speed, and accessibility of disease diagnosis, ultimately leading to improved patient outcomes and the delivery of more personalized, data-driven healthcare solutions.This review delves into the underlying factors driving the widespread adoption of deep learning in medical diagnosis, including advancements in computational power, the proliferation of large-scale, high-quality medical datasets, the development of sophisticated neural network architectures, and the collaborative efforts of interdisciplinary teams. It then explores the transformative impact of deep learning across various disease domains, such as cancer, neurological disorders, cardiovascular diseases, and infectious diseases, highlighting the impressive performance of these algorithms in outperforming traditional diagnostic methods.However, the successful integration of deep learning into clinical practice requires navigating several key challenges and considerations, including data availability and quality, model interpretability and transparency, regulatory approval and deployment, and ethical concerns surrounding data privacy, algorithmic bias, and healthcare equity. By addressing these critical issues, the healthcare industry can unlock the full potential of deep learning to revolutionize disease diagnosis, enabling earlier detection, more personalized treatment strategies, and ultimately, a healthier and more resilient future for patients worldwide.

KEYWORDS

Deep learning; Medical diagnosis; Disease detection; Precision medicine; Healthcare innovation; Artificial intelligence; Computer-aided diagnosis

CITE THIS PAPER

Liam Nguyen. The transformative potential of deep learning in revolutionizing medical diagnosis. World Journal of Engineering Research. 2024, 2(3): 1-7. DOI: https://doi.org/10.61784/wjer3007.

REFERENCES

[1] Holzinger A, Langs G, Denk H, et al. Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019, 9(4): e1312.

[2] Engestr?m Y. Objects, contradictions and collaboration in medical cognition: an activity-theoretical perspective. Artificial intelligence in medicine, 1995, 7(5): 395-412.

[3] Wang X, Wu Y C, Ma Z. Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 2021, 7: 1306058.

[4] Ricci Lara, M A, Echeveste R, et al. Addressing fairness in artificial intelligence for medical imaging. nature communications, 2022, 13(1): 4581.

[5] Ma Z, Chen X, Sun T, et al. Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization. Future Internet,  2024, 16(5): 163.

[6] Wang X, Wu Y C, Ji X, et al. Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 2024, 7: 1320277.

[7] Allen Jr B, Seltzer S E, Langlotz C P, et al. A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. Journal of the American College of Radiology, 2019, 16(9): 1179-1189.

[8] Wang X, Zhang X, Hoo V, et al. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access, 2024.

[9] Liu M, Ma Z, Li J, et al. Deep-Learning-Based Pre-training and Refined Tuning for Web Summarization Software. IEEE Access, 2024.

[10] Mesko, B. The role of artificial intelligence in precision medicine. Expert Review of Precision Medicine and Drug Development, 2017, 2(5): 239-241.

[11] Wang X, Wu Y C, Zhou M, et al. Beyond surveillance: privacy, ethics, and regulations in face recognition technology. Frontiers in big data, 2024, 7: 1337465.

[12] Chen X, Liu M, Niu Y, et al. Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization. IEEE Access, 2024.

[13] Hamamoto R, Suvarna K, Yamada M, et al. Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers, 2020, 12(12): 3532.

[14] Wang X, Wu Y C.  Balancing innovation and Regulation in the age of geneRative artificial intelligence. Journal of Information Policy, 2024, 14.

[15] Li J, Fan L, Wang X, et al.  Product Demand Prediction with Spatial Graph Neural Networks. Applied Sciences, 2024, 14(16): 6989.

[16] Sun T, Yang J, Li J, et al. Enhancing Auto Insurance Risk Evaluation with Transformer and SHAP. IEEE Access, 2024.

[17] Wang X, Hoo V, Liu M, et al. Advancing legal recommendation system with enhanced Bayesian network machine learning. Artificial Intelligence and Law, 2024: 1-18.

[18] Schneeberger D, St?ger K, Holzinger A. The European legal framework for medical AI. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2020: 209-226.

[19] Wang X, Wu Y C.  Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization. PloS one, 2024, 19(10): e0312623.

[20] Flores M J, Nicholson A E, Brunskill A, et al. Incorporating expert knowledge when learning Bayesian network structure: a medical case study. Artificial intelligence in medicine, 2011, 53(3): 181-204.

[21] Chen J, Cui Y, Zhang X, et al. Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences, 2024, 14(20): 9213.

[22] Zuo Z, Niu Y, Li J, et al. Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants. Applied Sciences,  2024, 14(18): 8442.

[23] Haug C J, Drazen J M. Artificial intelligence and machine learning in clinical medicine, 2023. New England Journal of Medicine, 2023, 388(13): 1201-1208.

[24] Asif M, Yao C, Zuo Z, et al. Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation. Journal of Industrial and Engineering Chemistry, 2024.

[25] Rector A L, Bechhofer S, Goble C A, et al. The GRAIL concept modelling language for medical terminology. Artificial intelligence in medicine, 1997, 9(2): 139-171.

[26] Lin Y, Fu H, Zhong Q, et al. The influencing mechanism of the communities’ built environment on residents’ subjective well-being: A case study of Beijing. Land, 2024, 13(6): 793.

[27] Allen Jr B, Seltzer S E, Langlotz C P, et al. A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. Journal of the American College of Radiology, 2019, 16(9): 1179-1189.