Artificial Intelligence and Deep Learning for Screening and Risk Assessment of Cancers

Authors

  • Mehrdad Farrokhi
  • Soheila Jafari Khouzani
  • Masoud Farrokhi
  • Hediyeh Jalayeri
  • Pooya Faranoush
  • Mahdi Babaei
  • Shadi Nouri
  • Mehrdad SalekShahabi
  • Mohammad Javad Taghipour
  • Fatemeh Tavakoli
  • Erfan Kohansal
  • Mohammad Khosousi Sani
  • Atousa Moghadam Fard
  • Sahba Emtehani
  • Roya Khorram
  • Mehdi Lotfinezhad
  • Habib Azimi
  • Nazanin Zafarani
  • Saharnaz Esmaeili
  • Yalda Zhoulideh
  • Soheil Shahbazi
  • Tara Mahmoodi
  • Zahra Pirouzan
  • Mahmonir Bayanati
  • Alireza Ghajary
  • Navid Zandi Atashbar
  • Mozhdeh Mohammadi Visroudi
  • Arnoosh Karimimoghadam
  • Behnoosh Sabaghi
  • Erfan Bozorgzade Ahmadi
  • Ehsan Fayyazishishavan
  • Amir Ghaleh Ghafi
  • Hournaz Hassanzadeh
  • Bahare Firouzbakht
  • Negar Radpour
  • Hamidreza Momeni
  • Shahriar Zohourian Shahzadi
  • Sahar Sanjarian
  • Shamim Chinian
  • Mona Mohajer Tehrani
  • Ali Ebrahimi
  • Zahrasadat Rezaei
  • Babak Goodarzy
  • Amir Moeini
  • Fatemeh Taheri
  • Sahar Hassantash

Keywords:

Artificial Intelligence , Deep Learning , Screening , Cancers

Abstract

Artificial Intelligence (AI) and Deep Learning have emerged as revolutionary tools in the domain of cancer screening and risk assessment. Leveraging vast amounts of data, these technologies offer a paradigm shift in early detection, diagnosis, and personalized treatment strategies for various cancers. Deep Learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in analyzing medical images like X-rays, MRIs, and CT scans. Their ability to detect subtle patterns and anomalies within images aids in identifying potential malignancies at their nascent stages. AI-driven algorithms assist radiologists in interpreting scans with higher accuracy and speed, enabling timely interventions and reducing human error. Moreover, AI's capacity to process extensive patient data allows for precise risk assessment. By analyzing diverse patient information, including genetic predispositions, lifestyle factors, and biomarkers, AI models can predict an individual's susceptibility to specific cancers. This facilitates early intervention or proactive measures to mitigate risks, enhancing preventive healthcare strategies. The integration of AI and Deep Learning in cancer screening not only enhances accuracy but also improves the efficiency of healthcare systems. Rapid analysis of large datasets expedites decision-making processes, optimizing resource allocation and improving patient outcomes. However, continual refinement and validation of AI algorithms with diverse and representative datasets are crucial to ensure reliability and mitigate biases. Ethical considerations surrounding data privacy and patient consent also warrant careful attention in deploying these technologies within healthcare settings. In conclusion, AI and Deep Learning technologies hold immense promise in transforming cancer screening and risk assessment, offering a new frontier in early detection and personalized care, thereby potentially saving numerous lives.

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Artificial Intelligence and Deep Learning for Screening and Risk Assessment of Cancers

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2024-01-04

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Farrokhi, M., Jafari Khouzani, S., Farrokhi, M., Jalayeri, H., Faranoush, P., Babaei, M., Nouri, S., SalekShahabi, M., Taghipour, M. J., Tavakoli, F., Kohansal, E., Khosousi Sani, M., Moghadam Fard, A., Emtehani, S., Khorram, R., Lotfinezhad, M., Azimi, H., Zafarani, N., Esmaeili, S., Zhoulideh, Y., Shahbazi, S., Mahmoodi, T., Pirouzan, Z., Bayanati, M., Ghajary, A., Zandi Atashbar, N., Mohammadi Visroudi, M., Karimimoghadam, A., Sabaghi, B., Bozorgzade Ahmadi, E., Fayyazishishavan, E., Ghaleh Ghafi, A., Hassanzadeh, H., Firouzbakht, B., Radpour, N., Momeni, H., Zohourian Shahzadi, S., Sanjarian, S., Chinian, S., Mohajer Tehrani, M., Ebrahimi, A., Rezaei, Z., Goodarzy, B., Moeini, A., Taheri, F., & Hassantash, S. (2024). Artificial Intelligence and Deep Learning for Screening and Risk Assessment of Cancers. Kindle, 4(1), 1–140. Retrieved from https://preferpub.org/index.php/kindle/article/view/book31

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