Introduction

Cancer is a disease caused by the uncontrolled division of abnormal cells in a part of the body. Instead of undergoing programmed cell death, also known as apoptosis, these damaged cells continue to divide forming tumors that can invade surrounding tissues and spread throughout the body. The National Cancer Institute reported in 2025 that approximately 38.9% of men and women will be diagnosed with cancer at some point during their lifetimes, with breast, prostate, lung, and colorectal cancers being the most common. Cancer has been named a leading cause of death worldwide with nearly 20 million new cases each year (NCI, 2025). While healthcare professionals work tirelessly to support and treat patients, and scientists continue searching for more effective therapeutics and potential cures, millions of individuals continue to face the life-altering challenges associated with this disease. However, recent technological advancements and scientific breakthroughs are beginning to transform the medical field by creating new ways to diagnose, treat, and even prevent cancer. One of the most significant developments is the rise of artificial intelligence (AI) in healthcare. Although there is ongoing debate about the advantages and limitations of this technology, growing evidence suggests that AI can improve patient outcomes, increase the efficiency and accuracy of diagnoses, and open new possibilities for cancer prevention and research.

Connection to sts

AI in oncology connects to STS because it shows how new technological developments directly impact society through healthcare. AI has become integrated into healthcare, specifically through the diagnosis, prevention, and treatment of cancer improving patient outcomes. This technology increases accuracy and enhances the abilities of existing medical devices, computer programs and medicines. AI is changing how scientists study cancer, as well as the operation of healthcare systems, decision making strategies, and the overall patient experience. In general, AI is a growing technology that improves scientists and healthcare professionals’ understanding of cancer, allowing for progress in oncology that benefits the health of society.

the role of AI in Oncology

Diagnosis

Artificial intelligence has been shown to improve cancer detection and early diagnosis. AI enhances cancer diagnosis by improving detection accuracy, reducing errors, and allowing for earlier identification. AI powered tools are being integrated into healthcare making diagnosis a faster process and allowing more patients to receive information and instruction at a time. This increased efficiency is essential because when cancer is caught at its earliest stages, the outcomes tend to be much more positive. Additionally, AI can analyze medical imaging much more efficiently and accurately than physicians (Chouvarda, 2025). The technology is able to identify small abnormalities at earlier stages that are incredibly difficult for doctors to catch. Specifically in breast cancer, the most common cancer in women, AI improves medical imaging, tumor classification, and early stage detection. AI has the ability to analyze mammograms with higher precision than radiologists while also reducing false positives and false negatives (McKinney, 2020). This technology is critical in making accurate assessments of data and testing in a timely manner.

Treatment

AI contributes to treatment by curating personalized medicine, improving various drug therapeutics, and predicting outcomes. AI is able to develop personalized treatment plans by analyzing genetic components of tumors, patient history and health data as well as analyzing previous patient outcomes (Ahn, 2023). By using AI to analyze images and patient samples, doctors are able to predict cancer behavior, hypothesize a treatment response, and choose the most effective treatment plan for each individual patient (Tran, 2019). The selection of the treatment is decided by AI because it is able to determine how the patient’s body will respond to the chemotherapy, radiation, or immunotherapy (Gujarat Report, 2026). Without this technology, it is difficult to predict how different patients will be effected by the powerful and often harmful drugs administered during cancer treatment. Furthermore, artificial intelligence increases the efficiency of drug discovery and development as part of the ground breaking cancer research being conducted (Udristoiu, 2025). Various initiatives build systems that can simulate treatment strategies to experiment with new therapies and improve existing ones (City of Hope, 2025). By aiding in the production of new drugs and modifying old ones, AI helps to improve the effectiveness of the numerous cancer treatments available.

Prevention

Cancer researchers have begun using AI to prevent cancer from developing and spreading, attempting to decrease the amount of people faced with this disease. This is accomplished through improving risk predictions and identifying early biological warning signs of cancer development. Artificial intelligence is able to identify high risk groups allowing for earlier screening and prevention strategies by analyzing large data sets of health records, genetic information, and lifestyle tendencies (Udristoiu, 2025). On a larger scale, these tools conduct population surveillance, helping public health researchers track trends of risk factors using national data (National cancer Institute, 2024). Overall, AI is an essential component of the research that strives to limit cancers’ impact on individuals and their quality of life.

Challenges and controversy

The integration of AI into healthcare has raised ethical concerns and a lack of trust in the healthcare system. AI systems use very large data sets that include patient information such as medical history, genetic data, and more. This has sparked controversy over the technologies rights to this information and lack of patient consent. Patients are concerned with data breaches and misuse of their private medical data (Price, 2019). Additionally, there are challenges in AI’s ability to limit bias. Like many technologies, artificial intelligence contains bias based on the perspectives of those that created it and the data sets it contains. Missing voices include racial and ethnic minorities, patients from low income backgrounds, and people in rural communities. If datasets lack diversity, AI can lead to worsening healthcare disparities between race, gender, and socioeconomic status (Obermeyer, 2019). It will also be less accurate in identifying and treating disease for underrepresented groups leading to unequal treatment outcomes.  This identifies the impact of social constructivism; a theory suggesting that technology is shaped by social factors such as values, human decisions, and cultural contexts. Furthermore, there are limitations to access and equity of this advanced technology. Due to high costs, low-income regions wont benefit equally, causing global inequities in cancer care to worsen if these tools cannot be accessible for diverse populations. Overall, AI tools may not be practical or accessible in all settings leading to minimal real world effectiveness. Although AI can dramatically benefit patient outcomes in some communities, it has the potential to reinforce health disparities.

Conclusion

In conclusion, cancer continues to be one of the most complex and challenging diseases affecting millions of people worldwide. As researchers and healthcare professionals work to better understand and treat this disease, artificial intelligence has demonstrated its potential to revolutionize oncology. By improving the accuracy and efficiency of diagnoses, aiding scientists in analyzing data and developing more personalized treatment plans, AI has the potential to significantly improve patient outcomes. While it is important to discuss the ethical concerns and limitations of this technology, the benefits it offers to the medical field are substantial. It is essential to ensure that diverse voices are represented in AI to create equitable and effective advancements in oncology. In the years to come, the integration of artificial intelligence into cancer research will likely play an increasingly important role and continue to act as a bridge between science, technology, and society.

References

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Artera: Cancer Innovation | Leading AI in Oncology. (n.d.). Artera. https://artera.ai/?CID=JSJDGoogleCPC&campaignid=23516484689&adgroupid=190270632657&keyword=ai%20oncology%20diagnostics&device=c&gad_source=1&gad_campaignid=23516484689&gbraid=0AAAABCvlXBx_HCHo-VSH3oBw0iXyD4JK5&gclid=Cj0KCQiA7-rMBhCFARIsAKnLKtCDZjfu3p23QAG9

Cancer Statistics – NCI. (2025, May 7). National Cancer Institute. Retrieved March 27, 2026, from https://www.cancer.gov/about-cancer/understanding/statistics

Chouvarda, I., Colantonio, S., Tsakou, G., & Yang, G. (Eds.). (2025). Trustworthy AI in Cancer Imaging Research. Springer Nature Switzerland.

City of Hope. (2025, May 6). Dr. Villarreal: Building the First AI Agent for Cancer Research | On the Edge of Breakthrough. Youtube. https://www.youtube.com/watch?v=Nh9hMszwM5E

McKinney, S.M., Sieniek, M., Godbole, V. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020). https://doi.org/10.1038/s41586-019-1799-6

Obermeyer, Z. (2019, October 25). Dissecting racial bias in an algorithm used to manage the health of populations. Science. https://www.science.org/doi/10.1126/science.aax2342

Price, W. (2019, January 7). Privacy in the age of medical big data. Nature. https://www.nature.com/articles/s41591-018-0272-7

Researcher from Gujarat Cancer & Research Institute Describes Findings in Artificial Intelligence (The Evolving Landscape of Artificial Intelligence in Cancer Research & Precision Medicine: Emerging Trends, Challenges and Opportunities). (2026). Obesity, Fitness & Wellness Week, 5637. https://link-gale-com.libproxy.clemson.edu/apps/doc/A872463343/AONE?u=clemsonu_main&sid=bookmark-AONE&xid=57b4f47f

Tran, W. T. (2019, December). Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S1939865419304333

Udriștoiu, A. L., & Udriștoiu, Ș. (2025). The Use of Artificial Intelligence (AI) Technologies in Biomedicine. Applied Sciences, 15(23), 12604. https://doi.org/10.3390/app152312604

AI Use Acknowledgements

I used Microsoft Co-Pilot and ChatGPT to find sources containing information about the role of AI in cancer research that supports the goals of this textbook chapter. They provided sources from various databases, websites, and journals, and extracted key details from articles.

scite.ai. (2023). Co-Pilot [Large Language Model].  https://copilot.microsoft.com

https://chatgpt.com/