The fight against cancer demands tools capable of improving diagnostic accuracy and facilitating increasingly early detection. In this context, Artificial Intelligence (AI) has become a valuable support for healthcare professionals, helping to analyze large volumes of medical information and identify patterns that are difficult to detect using conventional methods.
This article has been reviewed by Dr. Delia Cortés, Director of IVOQA.
How Does an AI “Think” to Identify a Tumor?
Artificial Intelligence systems applied to medicine analyze medical images using advanced Deep Learning algorithms and Convolutional Neural Networks (CNNs). These models are trained on thousands of images of healthy and pathological tissues to learn to recognize patterns associated with different diseases.
Far from replacing the specialist, AI acts as a supporting tool that helps identify suspicious areas, optimizes analysis times, and contributes to improving diagnostic accuracy.
Four Areas of Oncology Where AI is Transforming Diagnosis
Breast Cancer: A Tool to Improve Diagnostic Accuracy
Mammography remains the primary screening tool for breast cancer. Various studies have shown that AI can help reduce both false positives and false negatives, improving the detection of suspicious lesions and reducing the workload of radiologists.
Its main advantages include:
- Greater ability to detect subtle findings in complex mammograms.
- Reduction of unnecessary follow-ups resulting from false positives.
- Support for the specialist as a second clinical reader.
- Optimization of test interpretation times.
Lung Cancer: Advanced Thoracic Image Analysis
Lung cancer often presents symptoms only when the disease is already advanced. Therefore, early detection through low-dose computed tomography (LDCT) is essential.
AI models developed for the analysis of chest CT scans have shown a high capacity to identify suspicious lung nodules and prioritize studies that require a more urgent review. In addition, they allow large volumes of images to be analyzed in a matter of seconds, significantly streamlining the diagnostic process.
Liquid Biopsy: Artificial Intelligence Applied to Tumor DNA
The combination of Machine Learning algorithms with the analysis of cell-free DNA (cfDNA) opens new possibilities for early cancer detection.
Several studies have shown that computational analysis of tumor DNA fragmentation patterns can identify signals compatible with different types of tumors even before they become visible through conventional imaging tests.
Although this technology is still under development, it represents one of the most promising lines of research in the field of precision oncology.
Oncological Dermatology: Support in Skin Cancer Detection
AI has also shown highly relevant results in the analysis of skin lesions.
Computer vision-based systems can compare dermatological images with millions of previously analyzed examples, achieving performance levels comparable to those of expert dermatologists in certain scenarios.
This capability can facilitate faster detection of suspicious lesions and expedite referral for specialized evaluation.
Traditional Diagnosis vs. AI-Assisted Diagnosis
| Clinical Criteria | Traditional Methods | AI-Assisted Diagnosis |
|---|---|---|
| Analysis time | Variable depending on the complexity of the study | Automated processing in seconds |
| Detection of complex patterns | Dependent on clinical experience | Ability to identify subtle patterns difficult to appreciate visually |
| Decision-making support | Based exclusively on human interpretation | Complementary tool to support the specialist |
| Management of large volumes of studies | Requires longer review time | Allows prioritizing suspicious cases and optimizing resources |
Pancreatic Cancer
A study published in Gut by researchers at Mayo Clinic demonstrated that the REDMOD artificial intelligence model was able to identify signs of pancreatic cancer on CT scans performed up to years before clinical diagnosis, detecting 73% of pre-diagnostic cases and doubling the sensitivity of specialists in certain situations.
Ethical Challenges: A Supporting Tool, Not a Substitute
Despite its enormous potential, Artificial Intelligence does not replace the physician.
Diagnostic and therapeutic decisions continue to depend on the clinical judgment of the specialist, who integrates medical information, history, physical examination, and the individual context of each patient.
For this reason, one of the current priorities is to develop increasingly transparent and explainable models, capable of showing which variables have influenced each result. This philosophy seeks to avoid the so-called “black box” phenomenon and promote a safe and responsible use of technology.
Note: This article is for informational purposes only and does not replace a specialist’s diagnosis. If you have any questions, please visit a Viamed center.
Frequently Asked Questions (FAQs)
Scientific References:
- • LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature. 2015.
- • McKinney SM et al. International evaluation of an AI system for breast cancer screening. Nature. 2020. [View source]
- • Nature Medicine. Prospective evaluation of AI in breast cancer screening. 2025-2026. [View source]
- • Ardila D et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest CT. Nature Medicine. 2019. [View source]
- • Multi-Cancer Early Detection Tests: Current Evidence and Future Perspectives. [View source]
- • Karolinska Institutet. New method for detecting multiple cancers early. [View source]
- • Esteva A et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017.
- • Goenka A, Liu S, Chari ST, et al. Radiomics-based Early Detection Model (REDMOD) for prediagnostic pancreatic ductal adenocarcinoma detection on CT scans. Gut. 2026.
