Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging DOI Open Access
Mustaqueem Pallumeera,

Jonathan C. Giang,

Rajanbir Singh

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(9), P. 1510 - 1510

Published: April 30, 2025

Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning machine learning, excel in risk assessment, tumor detection, classification, predictive prognosis. Machine algorithms, especially frameworks, improve lesion characterization automated segmentation, leading to enhanced radiomic feature extraction delineation. Radiomics, which quantifies imaging features, offers personalized response predictions across various modalities. AI models also facilitate technological improvements non-diagnostic tasks, such as image optimization medical reporting. Despite advancements, challenges persist integrating into healthcare, tracking accurate data, ensuring patient privacy. Validation through clinician input multi-institutional studies essential safety model generalizability. This requires support from radiologists worldwide consideration of complex regulatory processes. Future directions include elaborating on existing optimizations, advanced techniques, improving patient-centric medicine, expanding healthcare accessibility. can enhance optimizing precision medicine outcomes. Ongoing multidisciplinary collaboration between radiologists, oncologists, software developers, bodies crucial AI's growing role clinical oncology. review aims provide an overview the applications oncologic while discussing their limitations.

Language: Английский

Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging DOI Open Access
Mustaqueem Pallumeera,

Jonathan C. Giang,

Rajanbir Singh

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(9), P. 1510 - 1510

Published: April 30, 2025

Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning machine learning, excel in risk assessment, tumor detection, classification, predictive prognosis. Machine algorithms, especially frameworks, improve lesion characterization automated segmentation, leading to enhanced radiomic feature extraction delineation. Radiomics, which quantifies imaging features, offers personalized response predictions across various modalities. AI models also facilitate technological improvements non-diagnostic tasks, such as image optimization medical reporting. Despite advancements, challenges persist integrating into healthcare, tracking accurate data, ensuring patient privacy. Validation through clinician input multi-institutional studies essential safety model generalizability. This requires support from radiologists worldwide consideration of complex regulatory processes. Future directions include elaborating on existing optimizations, advanced techniques, improving patient-centric medicine, expanding healthcare accessibility. can enhance optimizing precision medicine outcomes. Ongoing multidisciplinary collaboration between radiologists, oncologists, software developers, bodies crucial AI's growing role clinical oncology. review aims provide an overview the applications oncologic while discussing their limitations.

Language: Английский

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