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Artificial Intelligence (AI) and Cancer

Abstract illustration depicting artificial intelligence in medical sciences.

AI presents an unprecedented opportunity to advance our understanding of cancer and improve care for people with cancer.

Credit: iStock

Artificial intelligence (AI) is a machine’s ability to perform functions that are usually thought of as intelligent human behaviors, such as learning, reasoning, and solving problems. Computers derive this ability from algorithms that enable the use of data to make predictions or to create new content. AI algorithms can detect patterns in large amounts of data and identify relationships among pieces of data that cannot be easily perceived by the human brain.

In recent years, advances in three areas—methods and algorithms for training AI models, computer hardware needed to train these models, and access to large volumes of cancer data such as imaging, genomics, and clinical data—have converged, leading to promising new applications of AI in cancer research.

These new applications include understanding and predicting biological mechanisms, finding and leveraging patterns in clinical data to improve patient outcomes, and disentangling complex epidemiological, behavioral, and real-world data. Implemented in an ethical and scientifically rigorous manner, these uses of AI have the potential to rapidly advance cancer research and create better health outcomes for all.

AI applications in cancer research and care

NCI research is advancing the use of AI across the spectrum of cancer research and care, including mechanisms of cancer, cancer screening and diagnosis, drug discovery, cancer surveillance, and health care delivery.

Advancing fundamental knowledge of cancer biology

AI methods are being applied to advance knowledge about mechanisms of cancer initiation, progression, and metastasis. For example:

  • The body of scientific literature provides a vast resource of information and knowledge on cancer. Artificial intelligence experts are taking advantage of large language models to develop new computational tools to improve knowledge extraction from research publications.
  • As part of the collaboration between NCI and the Department of Energy, researchers are using AI to simulate the atomic behavior of the RAS protein, one of the most commonly mutated proteins in cancer. A better understanding of how RAS interacts with other proteins could help scientists find new avenues to target cancer-causing mutations in the RAS gene.

Expediting cancer screening, detection, and diagnosis

AI is helping to improve the speed, accuracy, and reliability of some cancer screening and detection methods. For example:

Accelerating cancer drug discovery

AI is being used in many ways to develop new treatments for cancer through novel approaches to drug discovery and design, drug repurposing, and predicting patient responses to treatment. For example:

Facilitating precision cancer treatment

Precision oncology is an approach to cancer care in which information about a tumor, such as tumor biomarkers, are used to guide treatment. This form of cancer care often involves analyzing a large amount of data with advanced computational approaches to help physicians make decisions. For example:

Improving cancer surveillance 

Cancer surveillance is the ongoing collection of patient information and cancer statistics. AI methods are being used to accelerate information extraction for surveillance reporting and to identify patterns in population-level cancer data. For example:

Improving access to cancer care

AI tools could also help make high-quality care accessible to more patients, even those who live far from cancer specialists or in low-resource settings, potentially helping to reduce cancer health disparities.

Challenges and opportunities for AI in cancer research

AI presents an unprecedented opportunity for rapid advances in understanding of cancer biology and optimization of patient care. However, if data used to train AI models are not appropriately diverse and representative of the broader population, these models can perpetuate medical bias. There is a need for broadly accepted and adopted standards for development of AI and machine learning models to mitigate bias and ensure reproducibility.

There is also a need for further randomized clinical trials to validate applications of AI and machine learning technologies in clinical practice. And advancing explainable artificial intelligence will be essential to integrate AI and machine learning technologies into clinical workflows. 

NCI is committed to supporting research aimed at addressing these challenges and advancing the development of AI methods that will accelerate the effort to end cancer as we know it.