AI-Driven Diagnostics and Imaging: Transforming Early Detection and Precision in Healthcare

Sriram Sitaraman

doi:10.32628/CSEIT241061167

Authors

Sriram Sitaraman SASTRA University, India Author

DOI:

https://doi.org/10.32628/CSEIT241061167

Keywords:

Medical Imaging AI, Healthcare Diagnostics, Deep Learning Architectures, Clinical Workflow Optimization, Healthcare Data Security

Abstract

Artificial intelligence is revolutionizing medical imaging and diagnostics, marking a transformative era in healthcare delivery. This comprehensive article explores the evolution from early computer-aided diagnosis systems to sophisticated deep-learning architectures, examining their impact across radiology, pathology, and clinical workflows. The article covers breakthrough technologies, including vision transformers, multi-modal integration, and explainable AI frameworks, highlighting their contributions to improved diagnostic accuracy and efficiency. The article encompasses the clinical benefits of early disease detection, workflow optimization, and cost reduction while addressing crucial challenges in regulatory compliance, ethical considerations, and data privacy. Looking ahead, the review examines emerging trends in federated learning, infrastructure requirements, and the economic implications of AI implementation in healthcare settings, providing insights into the future landscape of AI-driven medical imaging.

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References

Kunio Doi, "Computer-aided diagnosis in medical imaging: Historical review, current status and future potential," Computerized Medical Imaging and Graphics, Volume 31, Issues 4–5, June–July 2007, Pages 198-211. Available: https://www.sciencedirect.com/science/article/abs/pii/S0895611107000262 DOI: https://doi.org/10.1016/j.compmedimag.2007.02.002

Mohammad Shehab et al., "Machine learning in medical applications: A review of state-of-the-art methods," Computers in Biology and Medicine, Volume 145, June 2022, 105458. Available: https://www.sciencedirect.com/science/article/abs/pii/S0010482522002505 DOI: https://doi.org/10.1016/j.compbiomed.2022.105458

Xuxin Chen et al., "Recent advances and clinical applications of deep learning in medical image analysis," Medical Image Analysis, Volume 79, July 2022, 102444. Available: https://www.sciencedirect.com/science/article/abs/pii/S1361841522000913 DOI: https://doi.org/10.1016/j.media.2022.102444

Saba Shafi, Anil V Parwani, "Artificial intelligence in diagnostic pathology," Diagn Pathol. 2023 Oct 3;18:109. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10546747/#:~:text=Accurate%20pathological%20diagnoses%20involve%20assessment,brings%20to%20the%20pathologists'%20diagnoses.

Kelei He et al., "Transformers in medical image analysis," Intelligent Medicine, Volume 3, Issue 1, February 2023, Pages 59-78. Available: https://www.sciencedirect.com/science/article/pii/S2667102622000717 DOI: https://doi.org/10.1016/j.imed.2022.07.002

Felix Krones et al., "Review of multimodal machine learning approaches in healthcare," Information Fusion, Volume 114, February 2025, 102690. Available: https://www.sciencedirect.com/science/article/pii/S1566253524004688 DOI: https://doi.org/10.1016/j.inffus.2024.102690

Anu Maria Sebastian, David Peter, "Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions," Life (Basel). 2022 Nov 28;12(12):1991. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9786074/#:~:text=AI%2Dbased%20systems%20can%20help,by%20identifying%20the%20risk%20factors DOI: https://doi.org/10.3390/life12121991

Junaid Bajwa, Usman Munir, Aditya Nori, Bryan Williams, "Artificial intelligence in healthcare: transforming the practice of medicine," Future Healthc J. 2021 Jul;8(2):e188–e194. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/ DOI: https://doi.org/10.7861/fhj.2021-0095

Ciro Mennella et al., "Ethical and regulatory challenges of AI technologies in healthcare: A narrative review," Heliyon, Volume 10, Issue 4, 29 February 2024, e26297. Available: https://www.sciencedirect.com/science/article/pii/S2405844024023284 DOI: https://doi.org/10.1016/j.heliyon.2024.e26297

Shiv Kumar Mudgal et al., "Real-world application, challenges and implication of artificial intelligence in healthcare: an essay," Pan Afr Med J. 2022 Sep 2;43:3. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9557803/#:~:text=Potential%20challenges%20of%20the%20application,systems%2Ftechnologies%20on%20large%20datasets

Pallavi Dhade and Prajakta Shirke, "Federated Learning for Healthcare: A Comprehensive Review," Eng. Proc. 2023, 59(1), 230, 9 February 2024. Available: https://www.mdpi.com/2673-4591/59/1/230 DOI: https://doi.org/10.3390/engproc2023059230

Narendra N Khanna et al., "Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment," Healthcare (Basel). 2022 Dec 9;10(12):2493. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9777836/ DOI: https://doi.org/10.3390/healthcare10122493