AI: Revolutionizing the Fight Against Cancer

Artificial Intelligence. Over time the term has been associated with the likeliness of mastermind robots and big data clouds, but in today’s society the term is perhaps best represented by prevalence. Chat GPT; Google Gemini; Microsoft Copilot; The modern age is more digitized than ever, and new uses for a variety of AI models seem to pop-up out of every corner of digital space. These AI models have been incorporated into various areas, and now, they’ve found their way into the medical field, revolutionizing the tactics humanity utilizes in one of its most complex biological battles: the fight against cancer.

The key to understanding how AI has been so influential in revolutionizing medical progress is in its capabilities: specifically its ability to process and analyze indefinitely large amounts of data. With these methods, AI has accumulated various medical roles such as early detection of invasive cancers, pattern identification from screening, and biomarker/genomics motif recognition. AI’s ability to search wide data sets was seen in 2023, when collaborative research between Harvard Medical School and the University of Copenhagen incorporated large amounts of data in the form of around 9 million patient records to successfully test an AI algorithm that could determine which patients had the highest risk of pancreatic cancer up to 3 years before their actual diagnosis. 

AI has also made great leaps in screening. Many of the limitations that come from screening data can be related to sample sizes being too small or not diverse enough. This is where AI can utilize its capabilities to analyze diverse screening data and return results that are more personalized for patients. Implementation of these methods can be seen in Prov-GigaPath, a deep learning model created by Providence Health System, Microsoft, and the University of Washington. A 2025 study done to determine the proficiency  of various AI detection models, showed that Prov-GigaPath was able to analyze whole-slide pathology images from over 30,000+ patients and complete 26 digital pathology tests through analysis of tissue samples. This allowed the model to predict mutations and apply subtyping to various cancers— essentially grouping cancers by cellular characteristics for better identification and treatment. 

Part of the way AI identifies and classifies these various cancers is due to its role in the discovery of biomarkers. Biomarkers are medical signals identified from analysis of biological material that can help us identify diseases in patients. AI can identify these biological beacons through pattern recognition in large data fields. One such application of AI in this field is that of the CancerSEEK blood test, which analyzes DNA and proteins that are released from cancer cells into the bloodstream to classify various types of cancers. The liquid biopsy involves AI analysis to detect these patterns and even predict localizations of the signalled cancers. 

Despite its benefits, AI also acts as a somewhat double edged blade in the metaphorical “fight against cancer”. AI has significant limitations in relation to ethics and limitations, for example it costs about $500 per individual for the CancerSEEK liquid biopsy, raising questions on the viability of access to this technology for the general population as well as worries on growth of a monetary class gap in terms of medical treatment. Alongside this, AI’s greatest asset: its ability to look through large amounts of data, can only be used for provided data bases. Therefore, if the patients that we collect data from fail to incorporate a diversity of ages, genders, ages, etcetera., then we may question how helpful these advancements are to the global population. AI also raises concerns about data privacy and HIPPA laws: can AI be as secure as a doctor’s confidentiality? This brings forth another interesting development in which AI is pitted against a doctor’s word as reliance on general AI models such as chatGPT has been used in medical terms, even though these models may not be providing advice that supports those of medical professionals.

Artificial intelligence. Revisiting the term, we can associate it with a medical breakthrough, a superhuman screening device, and a tool to use for the betterment of human kind. But ignoring the potential drawbacks and/or increasing our reliance on the opinions of AI in comparison to human voices leads to potential setbacks in our advancement. While new research and testing occurs, it becomes more evident that if we are to use AI to improve the medical field we need to balance its benefits and drawbacks to truly win the fight against cancer.