How AI Improves Early Cancer Detection: Focus on Precision, Speed, and Medical Impact
DOI:
https://doi.org/10.47611/jsrhs.v13i4.8296Keywords:
PET, NLP, Artificial Intelligence, Machine Learning, Cancer, Early Detection, Data Privacy, PrivacyAbstract
This study explores the potential of artificial intelligence (AI) in enhancing early cancer diagnosis by evaluating the accuracy and efficiency of AI-based diagnostic tools compared to traditional methods. Despite challenges such as data privacy and algorithmic bias, the research demonstrates AI’s superior performance in accuracy and speed, suggesting significant improvements in early detection and personalized treatment. The study applies AI methods, such as deep learning and machine learning models to diverse medical data types including imaging, genomics, and pathology using a broad collection of AI algorithms. Such research points to the widely varying ways AI systems vastly outperform conventional means of diagnoses in accuracy and speed, offering improved earlier detection and more tailored plans for patients.
Downloads
References or Bibliography
Ahn, J. S., Shin, S., Yang, S.-A., Park, E., Kim, K. H., Cho, S. I., & Kim, S. (2023). Artificial intelligence in breast cancer diagnosis and personalized medicine. Journal of Breast Cancer, 26(5). https://doi.org/10.4048/jbc.2023.26.e45
Arunkumar, S., Jayaraj, V., & Sivasamy, A. (2023). Healthcare’s new frontier: AI-driven early cancer detection for improved well-being. AIP Advances, 13(11). https://doi.org/10.1063/5.0177640
Bhavsar, P. D., Pandya, D., Patel, H., & Jadeja, A. (2024). Artificial intelligence applied to cancer detection: Potential and barriers. International Journal of Scientific Research in Science and Technology, 117–120. https://doi.org/10.32628/ijsrst52310666
Chen, X., Hu, Y., Xu, T., Yang, H., & Wu, T. (2024). Advancements in AI for oncology: Developing an enhanced YOLOv5-based cancer cell detection system. International Journal of Innovative Research in Computer Science and Technology, 12(2), 75–80. https://ijircst.org/view_abstract.php?title=Advancements-in-AI-for-Oncology:-Developing-an-Enhanced-YOLOv5-based-Cancer-Cell-Detection-System&year=2024&vol=12&primary=QVJULTEyNDA=
Dang, A., Dang, D., & Vallish, B. (2023). Extent of use of artificial intelligence & machine learning protocols in cancer diagnosis: A scoping review. Indian Journal of Medical Research, 0(0), 0. https://doi.org/10.4103/ijmr.ijmr_555_20
Deepa, R., ALMahadin, G., P. G., & Sivasamy, A. (2024). Early detection of skin cancer using AI: Deciphering dermatology images for melanoma detection. AIP Advances, 14(4). https://doi.org/10.1063/5.0188187
Díaz Cantón, E., Fernández Sande, J., Krakobsky, V., Rossi, M., & Cantón, E. (2023). AI-driven advancements in breast cancer: Transforming detection, diagnosis, treatment, and monitoring. Journal of Cancer Research and Reviews Reports, 5(5), 1–4. https://doi.org/10.47363/JCRR/2023(5)178
Eloy, C., Marques, A., Pinto, J., Pinheiro, J., Campelos, S., Curado, M., Vale, J., & Polónia, A. (2023). Artificial intelligence–assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Archiv. https://doi.org/10.1007/s00428-023-03518-5
EMJ Radiol. (2024). Cancer screening and detection: Can AI change the game? EMJ Radiology, 5(1), 12-14. https://doi.org/10.33590/emjradiol/LZIE6219
Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A., Ouamane, A., & Mansoor, W. (2023). AI in thyroid cancer diagnosis: Techniques, trends, and future directions. Systems, 11(10), 519. https://doi.org/10.3390/systems11100519
Jiang, Y., Wang, C., & Zhou, S. (2023). Artificial intelligence-based risk stratification, accurate diagnosis, and treatment prediction in gynecologic oncology. Seminars in Cancer Biology. https://doi.org/10.1016/j.semcancer.2023.09.005
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1). https://doi.org/10.1186/s12916-019-1426-2
Khanagar, S. B., Alkadi, L., Alghilan, M. A., Kalagi, S., Awawdeh, M., Bijai, L. K., Vishwanathaiah, S., Aldhebaib, A., & Singh, O. G. (2023). Application and performance of artificial intelligence (AI) in oral cancer diagnosis and prediction using histopathological images: A systematic review. Biomedicines, 11(6), 1612. https://doi.org/10.3390/biomedicines11061612
Keane, P. A., & Topol, E. J. (2018). With an eye to AI and autonomous diagnosis. npj Digital Medicine, 1(1). https://doi.org/10.1038/s41746-018-0048-y
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2021). A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-021-09648-w
Macheka, S., Ng, P. Y., Ginsburg, O., Hope, A., Sullivan, R., & Aggarwal, A. (2024). Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: A systematic review. BMJ Oncology, 3(1). https://doi.org/10.1136/bmjonc-2023-000255
Mahmood, H., Shaban, M., Rajpoot, N., & Khurram, S. A. (2021). Artificial intelligence-based methods in head and neck cancer diagnosis: An overview. British Journal of Cancer, 124(12), 1934–1940. https://doi.org/10.1038/s41416-021-01386-x
Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023). AI-powered diagnosis of skin cancer: A contemporary review, open challenges and future research directions. Cancers, 15(4), 1183. https://doi.org/10.3390/cancers15041183
Mitsala, A., Tsalikidis, C., Pitiakoudis, M., Simopoulos, C., & Tsaroucha, A. K. (2021). Artificial intelligence in colorectal cancer screening, diagnosis and treatment: A new era. Current Oncology, 28(3), 1581–1607. https://doi.org/10.3390/curroncol28030149
Rentiya, Z. S., Mandal, S., Inban, P., Vempalli, H., Dabbara, R., Ali, S., Kaur, K., Adegbite, A., & Intsiful, T. A. (2024). Revolutionizing breast cancer detection with artificial intelligence (AI) in radiology and radiation oncology: A systematic review. Cureus, 16(4). https://doi.org/10.7759/cureus.57619
Reviewing the role of artificial intelligence in cancer. (2020, December 7). Typeset.io. https://typeset.io/papers/reviewing-the-role-of-artificial-intelligence-in-cancer-4z34z4m3xx
Silva, Santos, Ruffeil, C., Leite, A. F., Stefani, C. M., & Melo, S. (2022). The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview. Research Square. https://doi.org/10.21203/rs.3.rs-2184114/v1
Swaroopa, K., et al. (2023). AI driven innovation in early detection and diagnosis of brain cancer. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 2738–2744. https://doi.org/10.17762/ijritcc.v11i9.9349
Tripathi, S., Tabari, A., Mansur, A., Dabbara, H., Bridge, C. P., & Daye, D. (2024). From machine learning to patient outcomes: A comprehensive review of AI in pancreatic cancer. Diagnostics, 14(2), 174–174. https://doi.org/10.3390/diagnostics14020174
Wale, A., Shaw, H., Ayres, T., Okolie, C., Morgan, H., Everitt, J., Little, K., Edwards, R. T., Davies, J., Lewis, R., Cooper, A., & Edwards, A. (2023). From machine learning to patient outcomes: A comprehensive review of AI in pancreatic cancer. Diagnostics, 14(2), 174–174. https://doi.org/10.1101/2023.11.09.23298257
Xiao, Z., Ji, D., Li, F., Li, Z., & Bao, Z. (2021). Application of artificial intelligence in early gastric cancer diagnosis. Digestion, 103(1), 69–75. https://doi.org/10.1159/000519601
Yu, C., & Helwig, E. J. (2021). The role of AI technology in prediction, diagnosis, and treatment of colorectal cancer. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-10034-y
Zhang, B., Shi, H., & Wang, H. (2023). Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach. Journal of Multidisciplinary Healthcare, 16(16), 1779–1791. https://doi.org/10.2147/JMDH.S410301
Zhang, B., Shi, H., & Wang, H. (2023). Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach. Journal of Multidisciplinary Healthcare, 16(16), 1779–1791. https://doi.org/10.2147/JMDH.S410301
Zhang, B., Shi, H., & Wang, H. (2023). Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach. Journal of Multidisciplinary Healthcare, 16(16), 1779–1791. https://doi.org/10.2147/JMDH.S410301
Zhang, Y., et al. (2021). AI-enhanced MRI for breast cancer detection. Medical Image Analysis. https://doi.org/10.1016/j.media.2021.102012
Ardila, D., et al. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954-961. https://doi.org/10.1038/s41591-019-0447-x
Rodriguez-Ruiz, A., et al. (2019). Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists. European Radiology, 29(3), 1649-1658. https://doi.org/10.1007/s00330-018-5815-5
Wu, G. G., et al. (2021). Deep learning-based ultrasound imaging for the diagnosis of thyroid nodules: A review of the current state-of-the-art. European Journal of Nuclear Medicine and Molecular Imaging, 48(6), 1830-1840. https://doi.org/10.1007/s00259-021-05434-z
Garcia, A., et al. (2021). Predictive modeling of lymphoma treatment outcomes using AI-enhanced PET scans. European Journal of Nuclear Medicine and Molecular Imaging, 48(5), 1253-1261. https://doi.org/10.1007/s00259-021-05432-1
Rajpurkar, P., et al. (2018). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225. https://arxiv.org/abs/1711.05225
Zhu, H., et al. (2019). DeepPET: A deep encoder-decoder network for direct whole-body PET image reconstruction. IEEE Transactions on Medical Imaging, 38(12), 2750-2762. https://doi.org/10.1109/TMI.2019.2920452
National Cancer Institute. (2020). Cancer screening overview (PDQ®)–Patient version. National Institutes of Health. https://www.cancer.gov/about-cancer/screening/patient-screening-overview-pdq
Published
How to Cite
Issue
Section
Copyright (c) 2024 Monish Malla; Dr. Monica Sava, Jothsna Kethar
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
