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Related Experiment Video

Updated: Jul 14, 2025

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Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review.

N Chakrabarty1, A Mahajan2

  • 1Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|October 8, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning are revolutionizing oncology by improving cancer screening, diagnosis, and treatment prediction. While clinical applications are emerging, ongoing research aims to overcome barriers for widespread AI adoption in cancer care.

Keywords:
Artificial intelligencecancerdeep learningdiagnosis-genomic mutations-outcome prediction

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Deep Learning
  • Medical Imaging

Background:

  • The surge in artificial intelligence (AI) research in oncology is driven by advancements in computing power, algorithms, and data availability.
  • Deep learning, particularly convolutional neural networks, is increasingly utilized for various cancer care applications.

Purpose of the Study:

  • To discuss the multifaceted role of deep learning in oncology.
  • To explore the applications of AI in cancer screening, diagnosis, treatment response prediction, and automated radiology reporting.
  • To address the challenges and future directions for clinical implementation of AI in cancer care.

Main Methods:

  • Review of current artificial intelligence and deep learning applications in oncology.
  • Discussion of convolutional neural networks for cancer risk stratification, diagnosis, and outcome prediction.
  • Exploration of radiomics, imaging biobanks, and automated radiology report generation.

Main Results:

  • Deep learning models show promise in diverse oncology tasks, including risk stratification, genomic mutation prediction, and treatment response assessment.
  • AI facilitates automated radiology report generation, potentially reducing turnaround times in high-volume settings.
  • AI applications in oncoimaging can provide valuable insights for cancer management at baseline, saving time and resources.

Conclusions:

  • While validated clinical AI models are still developing, ongoing research is paving the way for their broader implementation.
  • AI and radiomics offer significant potential to advance oncoimaging and cancer care.
  • Addressing commercialization and ethical considerations is crucial for the successful clinical translation of AI in oncology.