What is cancer?
Cancer is a disease characterized by the uncontrolled growth and spread of abnormal cells in the body. If not detected early, cancer can invade nearby tissues and metastasize to distant organs, making treatment more complex and less effective. According to the World Health Organization (WHO), cancer is a leading cause of death worldwide, responsible for nearly 10 million deaths each year. Early detection has been proven to significantly increase survival rates, since cancers diagnosed at an initial stage are often easier to treat, less invasive, and less costly.
Traditional detection methods include imaging, biopsies, and laboratory tests. While these approaches remain central in clinical practice, their limitations such as time, cost, and variability in accuracy have opened the door to new technologies. Artificial Intelligence (AI) has emerged as a transformative tool in this regard, offering the possibility of detecting subtle disease patterns invisible to the human eye.
Source: World Health Organization, 2025.
Pathophysiology
Cancer develops through a series of genetic and epigenetic changes that disrupt normal cell regulation. These changes allow malignant cells to evade apoptosis, promote angiogenesis, and metastasize. While these biological mechanisms are well-established, AI now enables deeper analysis of these processes.
AI systems integrate genomic, proteomic, and imaging data to identify early molecular signatures of cancer. This enhances both diagnostic precision and understanding of disease progression.
Source: National Cancer Institute, 2024.
Imaging-based AI detection
One of the most widely studied applications of AI in oncology is in medical imaging. Radiology has long been central to cancer diagnosis, but image interpretation is subject to human error and variability. AI, particularly deep learning algorithms, has been trained to recognize minute patterns in mammograms, CT scans, MRIs, and dermoscopic images that may indicate early cancerous changes.
Recent research shows that AI systems can match or even surpass radiologists in detecting breast cancer, lung nodules, and skin melanomas. In addition, the International Agency for Research on Cancer (IARC) has developed AI tools capable of detecting precancerous cervical lesions from cervix images, especially in low-resource settings where access to expert screening is limited. By increasing accuracy and accessibility, AI holds promise for reducing disparities in cancer detection worldwide.
Source: International Agency for Research on Cancer, 2024.
Genomic and molecular data
Advances in genomic sequencing have revealed the complex molecular basis of cancer. However, analyzing these massive datasets is a challenge that exceeds traditional statistical methods. AI has proven particularly useful in this domain, as it can process large volumes of genetic data to identify patterns associated with cancer initiation.
Machine learning models are currently used to predict mutations, such as BRCA1 and BRCA2 in breast cancer, and to identify molecular subtypes of lung and colorectal cancers. Moreover, AI is increasingly integrated into liquid biopsy technologies, which analyze circulating tumor DNA and other biomarkers in blood. This combination allows for the detection of cancers even before clinical symptoms appear.
Source: American Cancer Society, 2025.
Risk factors
The risk of developing cancer depends on a combination of genetic, environmental, and lifestyle factors. AI is playing a growing role in identifying how these factors interact. By analyzing electronic health records, lifestyle data, and genetic predispositions, AI can generate personalized risk profiles for individuals.
For example, AI models can evaluate smoking habits, obesity, diet, and radiation exposure alongside hereditary mutations to predict which populations are at higher risk. This not only improves targeted screening but also strengthens preventive strategies. Public health organizations emphasize that nearly 40% of cancers could be prevented by reducing modifiable risk factors, and AI is enhancing the ability to identify and monitor those risks.
Source: American Cancer Society, 2025.
Current treatment
Cancer treatment today includes surgery, chemotherapy, radiotherapy, immunotherapy, and targeted therapies. While these approaches have improved significantly, their effectiveness depends heavily on how early the cancer is detected. AI contributes by supporting oncologists in treatment planning, predicting responses to therapies, and reducing unnecessary treatments.
For instance, AI-based systems are now able to analyze pathology slides to predict the likelihood of metastasis in breast cancer, guiding more precise treatment strategies. Large cancer centers and research institutions are also implementing AI tools to match patients with the most suitable clinical trials, accelerating the development of new therapies.
Source: National Cancer Institute, 2025.
Relation between AI and early cancer detection
The greatest promise of AI in oncology lies in its ability to detect cancer early, often years before clinical symptoms appear. AI-assisted mammography has been shown to increase cancer detection rates compared to radiologists alone, while computer-aided colonoscopy reduces missed polyps during screening. AI tools are also being tested in pancreatic and liver cancers, both of which are typically diagnosed at advanced stages.
Emerging technologies, such as multi-cancer early detection (MCED) blood tests, are also being powered by AI to identify signals from circulating biomarkers. Though still under evaluation, these tests may one day allow population-wide screening for dozens of cancers with a single blood sample.
Source: World Health Organization, 2024.
Implications of AI in prevention and treatment
The integration of AI into cancer care has implications beyond diagnosis. At the population level, AI-driven screening programs can increase efficiency, reduce costs, and ensure more equitable access to care. At the clinical level, AI can guide personalized interventions, improving survival rates and quality of life for patients.
Despite its potential, challenges remain. Issues of data privacy, algorithm transparency, and bias in training datasets must be addressed before AI becomes fully embedded in health systems. The WHO has published guidance on evaluating AI-based medical technologies, emphasizing the need for rigorous testing and ethical standards. With appropriate safeguards, AI can help transform cancer prevention and treatment, offering hope for reducing the global burden of this disease.
Source: World Health Organization, 2025.
Future projections
AI is increasingly moving from experimental projects into real-world clinical applications. In Sweden, randomized clinical trials of AI-assisted mammography have already demonstrated a 20% increase in breast cancer detection compared to standard screening. In the United States, multi-cancer early detection blood tests are being tested in large-scale studies, with promising results for identifying cancers years before symptoms arise. IARC continues to expand its AI-powered cervical cancer screening tools to low- and middle-income countries, aiming to reduce global disparities in cancer diagnosis.
Looking ahead, experts anticipate that AI will not only complement but also reshape existing screening programs, allowing for earlier detection across multiple cancer types through integrated platforms that combine imaging, genomics, and blood-based biomarkers. If current trials confirm their effectiveness, these technologies are expected to become part of routine population-wide screening within the next decade, fundamentally changing how cancer is prevented, detected, and treated worldwide.
Source: World Health Organization, 2024.
