AI in Cancer Research

Artificial intelligence is rapidly embedding itself into cancer research and treatment, transforming what was once science fiction into clinical reality. By 2030, analysts project the global AI-in-healthcare market could surpass $187 billion, with oncology commanding a significant share of this growth. The excitement is understandable as cancer remains one of humanity’s deadliest burdens, responsible for millions of deaths worldwide. For every promise AI makes in this space, the stakes could not be higher.

Transforming Diagnosis and Drug Development

Current progress is already measurable. In diagnostic imaging, AI-enhanced mammography has boosted detection accuracy by 9%, reducing false negatives that can delay lifesaving treatment. Algorithms applied to pathology slides have identified cancers at rates rivaling top oncologists, and in some cases increased diagnostic sensitivity by 30%. At Penn Medicine, AI systems are finding malignant cells invisible to the human eye, highlighting tumors that would otherwise go untreated until much later stages. These technologies are not hypothetical; they are deployed today in pilot studies and hospital trials, shifting detection from reactive to proactive.

Drug development, historically a decade-long process with costs often exceeding $2.6 billion per therapy, is also being disrupted. Machine learning models can now analyze billions of molecular interactions within weeks, accelerating the pipeline for new anti-cancer compounds. Early reports from pharmaceutical partnerships show AI can cut research timelines by up to 70%, dramatically lowering costs and potentially bringing therapies to patients five to seven years faster than traditional approaches. A National Cancer Institute initiative recently demonstrated that AI-guided tumor profiling could predict the most effective therapy with greater than 80% accuracy, a leap toward hyper-personalised medicine.

Limits, Biases, and Global Inequalities

The notion of a universal “cancer cure” remains more marketing than reality. Cancer is not a single disease but a constellation of over 200 genetically distinct conditions, each with unique mutations, progression rates, and resistance mechanisms. Even the most advanced AI cannot yet account for the full complexity of patient genetics, immune system variability, and environmental influences. The result is that while AI can improve five-year survival rates – for example, by nearly 30% when used in early lung cancer screening programs, it does not guarantee elimination of the disease. Instead, the real transformation lies in precision, through matching the right therapy to the right patient at the right time.

Studies published in JAMA in 2024 revealed that data biases reduce AI accuracy in up to 25% of underrepresented populations, creating disparities in care. With nearly 70% of the world’s cancer burden occurring in low- and middle-income countries, models trained predominantly on Western datasets risk excluding the very regions where breakthroughs are most needed. Access to data is another barrier. Vast troves of clinical records, which could train more inclusive and powerful models, remain locked behind proprietary walls due to privacy and corporate restrictions. Unlocking this data safely will require advances in privacy-preserving computation, federated learning, and stronger global data-sharing frameworks.

Looking Ahead: Promise and Caution

Looking ahead, the projections are both ambitious and sobering. Analysts predict that by 2040, AI could contribute to cutting global cancer mortality by 15–20%, primarily through earlier detection and better treatment allocation. In breast cancer alone, wider deployment of AI-enhanced screening could prevent as many as 150,000 deaths annually worldwide. Yet experts caution that these outcomes depend not just on algorithms, but on equitable healthcare infrastructure, clinical oversight, and long-term investment in inclusive datasets.

The future of AI in oncology is neither a miracle cure nor a hollow promise. It is a measurable, accelerating force that can tilt the odds, reducing diagnostic errors, shrinking drug timelines, and pushing medicine toward personalisation on a scale previously impossible. The vision is not a world without cancer, but one in which survival is more likely, treatment is less punishing, and outcomes are no longer dictated by chance or geography. Artificial intelligence, if guided responsibly, may be the catalyst that reshapes cancer from a global killer into a disease that can be managed and in many cases, defeated.

By Chloe Maluleke 

Associate at The BRICS+ Consulting Group 

Russian & Middle Eastern Specialist 

** MORE ARTICLES ON OUR WEBSITE https://bricscg.com/

** Follow https://x.com/brics_daily on X/Twitter for daily BRICS+ updates