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Artificial Intelligence-Based Systems for Cancer Diagnosis: Trends and Future Prospects
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Cancer remains a leading cause of morbidity and mortality worldwide, necessitating advancements in early detection and accurate diagnosis to improve patient outcomes. Artificial Intelligence (AI)-based systems have emerged as transformative tools in oncology, leveraging machine learning (ML) and deep learning (DL) algorithms to enhance diagnostic accuracy, reduce human error, and facilitate personalized treatment strategies. This systematic review explores current trends and future prospects in AI-driven cancer diagnosis, highlighting key technologies, data sources, performance metrics, challenges, and innovations. AI technologies, including convolutional neural networks (CNNs), transformer-based models, and radiomics, have demonstrated remarkable efficacy in analyzing medical imaging modalities such as MRI, CT scans, and histopathological slides. Additionally, AI models integrate multi-omics data, electronic health records (EHRs), and real-time biosensor data to improve diagnostic precision. Performance evaluation metrics, including accuracy, sensitivity, specificity, and AUC-ROC, play a crucial role in validating AI models against traditional diagnostic methods. Despite its potential, AI implementation in cancer diagnosis faces challenges such as data standardization, model interpretability, and regulatory compliance (HIPAA, GDPR). Bias in AI models and the need for explainable AI remain critical concerns for clinical adoption. However, advancements in federated learning, predictive analytics, and AI-powered precision oncology are expected to address these limitations, fostering trust and integration into healthcare workflows. Future research should focus on enhancing AI-human collaboration, ensuring data security, and refining deep learning architectures for greater diagnostic efficiency. As AI continues to evolve, it holds immense promise in revolutionizing cancer diagnostics, ultimately improving early detection, reducing healthcare costs, and enabling more effective treatment planning. This review provides a comprehensive analysis of AI-driven cancer diagnostic systems, emphasizing their transformative role and the future trajectory of AI applications in oncology.
Keywords
Artificial intelligence, Cancer diagnosis, AI-powered precision oncology, Review
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How to cite this paper
@article{1703782,
author = {Damilola Osamika, Bamidele Samuel Adelusi, MariaTheresa Chinyeaka Kelvin-Agwu, Ashiata Yetunde Mustapha, Nura Ikhalea},
title = {Artificial Intelligence-Based Systems for Cancer Diagnosis: Trends and Future Prospects},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {2},
pages = {340-353},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703782.pdf},
abstract = {Cancer remains a leading cause of morbidity and mortality worldwide, necessitating advancements in early detection and accurate diagnosis to improve patient outcomes. Artificial Intelligence (AI)-based systems have emerged as transformative tools in oncology, leveraging machine learning (ML) and deep learning (DL) algorithms to enhance diagnostic accuracy, reduce human error, and facilitate personalized treatment strategies. This systematic review explores current trends and future prospects in AI-driven cancer diagnosis, highlighting key technologies, data sources, performance metrics, challenges, and innovations. AI technologies, including convolutional neural networks (CNNs), transformer-based models, and radiomics, have demonstrated remarkable efficacy in analyzing medical imaging modalities such as MRI, CT scans, and histopathological slides. Additionally, AI models integrate multi-omics data, electronic health records (EHRs), and real-time biosensor data to improve diagnostic precision. Performance evaluation metrics, including accuracy, sensitivity, specificity, and AUC-ROC, play a crucial role in validating AI models against traditional diagnostic methods. Despite its potential, AI implementation in cancer diagnosis faces challenges such as data standardization, model interpretability, and regulatory compliance (HIPAA, GDPR). Bias in AI models and the need for explainable AI remain critical concerns for clinical adoption. However, advancements in federated learning, predictive analytics, and AI-powered precision oncology are expected to address these limitations, fostering trust and integration into healthcare workflows. Future research should focus on enhancing AI-human collaboration, ensuring data security, and refining deep learning architectures for greater diagnostic efficiency. As AI continues to evolve, it holds immense promise in revolutionizing cancer diagnostics, ultimately improving early detection, reducing healthcare costs, and enabling more effective treatment planning. This review provides a comprehensive analysis of AI-driven cancer diagnostic systems, emphasizing their transformative role and the future trajectory of AI applications in oncology.},
keywords = {Artificial intelligence, Cancer diagnosis, AI-powered precision oncology, Review},
month = {August},
}