The evolving landscape: Role of artificial intelligence in cancer detection

Praveen Kumar; Sakshi V. Izankar; Induni N. Weerarathna; David Raymond; Prateek Verma; Praveen Kumar; Sakshi V. Izankar; Induni N. Weerarathna; David Raymond; Prateek Verma

doi:10.3934/bioeng.2024009

AIMS Bioengineering

2024, Volume 11, Issue 2: 147-172. doi: 10.3934/bioeng.2024009

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Review

The evolving landscape: Role of artificial intelligence in cancer detection

1.
Department of Computer Science and Medical Engineering, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed To Be University), Wardha, Maharashtra 442001, India
2.
Department of Computer Science and Design, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed To Be University), Wardha, Maharashtra 442001, India
3.
Department of Biomedical Sciences, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed To Be University), Wardha, Maharashtra, 442001, India
4.
Department of Artificial Intelligence and Machine Learning, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed To Be University), Wardha, Maharashtra 442001, India

Received: 07 January 2024 Revised: 10 May 2024 Accepted: 14 May 2024 Published: 16 May 2024

Artificial intelligence (AI) has played a major role in recent developments in healthcare, particularly in cancer diagnosis. This review investigated the dynamic role of AI in the detection of cancer and provides insights into the fundamental contributions of AI in the revolutionization of cancer detection methodologies, focusing on the role of AI in radiology and medical imaging and highlighting AI's advancements in enhancing accuracy and efficiency in identifying cancerous lesions. Furthermore, it explained the indispensable role of pathology and histopathology in cancer diagnosis, emphasizing AI's potential to augment traditional methods and improve diagnostic precision. Genomics and personalized medicine were explored as integral components of cancer detection, illustrating how AI facilitates tailored treatment strategies by analyzing vast genomic datasets. Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. Finally, the review addressed the challenges and future directions in the integration of AI into cancer detection practices, highlighting the need for continued research and development to overcome existing limitations and realize the full potential of AI-driven solutions in combating cancer.
- artificial intelligence,
- deep learning,
- machine learning,
- cancer detection
Citation: Praveen Kumar, Sakshi V. Izankar, Induni N. Weerarathna, David Raymond, Prateek Verma. The evolving landscape: Role of artificial intelligence in cancer detection[J]. AIMS Bioengineering, 2024, 11(2): 147-172. doi: 10.3934/bioeng.2024009

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Abstract

Artificial intelligence (AI) has played a major role in recent developments in healthcare, particularly in cancer diagnosis. This review investigated the dynamic role of AI in the detection of cancer and provides insights into the fundamental contributions of AI in the revolutionization of cancer detection methodologies, focusing on the role of AI in radiology and medical imaging and highlighting AI's advancements in enhancing accuracy and efficiency in identifying cancerous lesions. Furthermore, it explained the indispensable role of pathology and histopathology in cancer diagnosis, emphasizing AI's potential to augment traditional methods and improve diagnostic precision. Genomics and personalized medicine were explored as integral components of cancer detection, illustrating how AI facilitates tailored treatment strategies by analyzing vast genomic datasets. Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. Finally, the review addressed the challenges and future directions in the integration of AI into cancer detection practices, highlighting the need for continued research and development to overcome existing limitations and realize the full potential of AI-driven solutions in combating cancer.

Abbreviations

Artificial intelligence

WHO

World Health Organization

Deep learning

Machine learning

MRI

Magnetic Resonance Imaging

Computed Tomography

KNN

K-nearest neighbors

SVM

Support Vector Machine

CNN

Convolutional neural network

Neural network

GNN

Generative adversarial network

PET

Position Emission Tomography

FDA

Food and Drug Administration

CAD

Computer-assisted diagnosis

FFDM

Full Field Digital Mammography

AiCE

Advanced intelligent Clear IQ Engine

FBP

Filtered back projection

SPECT

Single-photon emission computed tomography

DWI

Diffusion-weighted imaging

Conflict of interest

The authors declare no conflicts of interest.

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