AI and Cancer: The Emerging Revolution January 14, 2025January 14, 2025 CRI Staff Artificial Intelligence (AI) is everywhere these days; it’s in our homes, cars, schools, workplaces, and seemingly every app we use. With how ubiquitous it is (and how liberally the term is used to describe everything in the tech world), it can be easy to forget that advancements in AI are making massive waves across many sectors, especially the ones you might not expect.. Healthcare is one such sector, and the search for a cure to cancer is at the forefront of these developments. While the fight against cancer is an ongoing challenge, AI is helping to reshape how we understand, diagnose, and treat it in ways never before thought possible. Working With What We Already Have While we don’t have all the answers yet, there’s no shortage of scientific literature on the disease. Decades of clinical trials, studies, papers, and more have left us with a seemingly infinite number of data points and takeaways. The trouble is humans aren’t computers. No one person can find, absorb, and recall every relevant piece of information available at any given time. Large Language Models (LLM) can, though. AI creates immeasurable efficiency when it comes to aggregating, recalling, and contextualizing complicated datasets without the element of human error. In other words, with a few short prompts, advanced models can: Process irrationally large amounts of data Identify patterns Make predictions Perform analysis that researchers would otherwise need to sleuth manually. Science, being fundamentally iterative in nature, can make better use of things we already know to progress, and cancer research is no exception. Perhaps even more notably, the future potential for this technology to streamline studies, analysis, trial design, and recruitment may well create an exponential impact on our race to the finish line for a cure. AI’s Role in Prevention Prevention and early detection are the most powerful weapons we have against cancer. But prevention is easier said than done, so to speak. With the vast number of contributing causes of cancer, and a radical variance in when and how early warning signs present, it can be hard to know when screening is necessary. Beyond that, access to screening remains a challenge, whether due to proximity to specialists, socioeconomic barriers, medical literacy, or something else. AI is making breakthroughs that could remove all these obstacles and more, and revolutionize prevention and early detection. In 2023, research was conducted to explore how AI could aid in predicting incidences of pancreatic cancer, which is traditionally more difficult and expensive to screen for compared to other cancers. Using disease codes and their timing of occurrence from millions of patient records, the model predicted which patients were at highest risk for developing pancreatic cancer, despite the fact that “many of the symptoms and disease codes were not directly related to or stemming from the pancreas.” The model was not only more accurate than population estimates, but was also believed to be “at least as accurate in predicting disease occurrence as are current genetic sequencing tests that are usually available only for a small subset of patients in data sets.” In short, this algorithm accomplished something that usually requires difficult-to-access genetic testing using millions of data points, many of which may have been entirely overlooked by a human. At scale, the possibilities are endless, and advanced models could very well aid the future of early detection by finding trends and relations between health, environmental, and behavioral factors that cause cancer. AI’s Role in Diagnosis Diagnosis may sound leaps and bounds more complicated than simple predictive analytics, but at its core there’s not much of a fundamental difference. While we’re not close to replacing doctors with friendly robots, the models are essentially doing what seasoned practitioners do: recalling information about a subject, comparing data, scans and charts, and making assumptions about them. This can be monumental for expedience and limiting invasive diagnostic procedures. One example is the case of a woman with a thyroid lump that wouldn’t go away. Upon examination of an ultrasound, her doctor ordered a biopsy of the lump (which turned out to be benign). She then went to another radiologist for a second opinion, who came to the same conclusion. The second radiologist, however, used AI-driven thyroid ultrasounds to make their determination. Had she started here, an invasive test with weeks of waiting for results could have been prevented. In another instance, Penn Medicine researchers developed a tool that is capable of detecting cancer cells that are easy to miss, or even invisible, to the eye. Beyond sheer precision, it’s able to analyze and reconstruct enormous amounts of data in a very short time. Perhaps the most promising fact is that AI can be trained to scan imaging like MRIs to identify and flag potential tumor-like structures in patients’ scans with incredible efficiency. This can help radiologists and oncologists who can do a deeper examination of these flagged areas. It may be a while before these tools become ubiquitous in healthcare settings, but it’s clear that there’s an emerging use case for machine learning to aid in the things that humans can’t do efficiently, quickly, or sometimes at all. AI’s Role in Treatment AI has emerged as a practical tool in cancer intervention and treatment in a variety of ways. We’ve already seen similar applications in this realm as we have with treatment and prevention. For example, a 2023 study showed that AI can have a crucial impact on precision medicine and the development of treatment plans. It did this by assisting in the prediction of treatment effects for tumor patients, personalizing treatment based on the unique cases of the patients, and much more as a result of mining “the deep-level information in genomics”. But it’s not all data-based; AI is becoming hands-on. It’s shown potential to optimize radiation doses, assist in surgical procedures, and offer on-the-fly adjustments to treatment plans that often need them. Further, its role in research cannot be understated. It’s not easy or cheap researching, developing, and bringing to market a new treatment or drug, however promising it may be. AI has the potential to not only streamline these processes, but make discovery and development more efficient than ever. One example of this is AlphaFold2, which, with breakthroughs in understanding protein structure and more, is said to enhance the speed and precision of drug target identification. Ethics, Limitations, and Challenges While Hollywood may have created exacerbated perceptions of AI, there are myriad genuine limitations and ethical dilemmas that will continue to emerge as it becomes further entrenched in the modern world. Data Privacy In the case of healthcare, data privacy and security concerns are more pronounced than almost any other sector. It’s no secret that the average person is blind to the sheer scope of their personal data that’s collected, stored, sold, and shared. In the realm of health, it remains pertinent that codified norms with health data don’t slowly become the next generation of behavioral analytics. This isn’t limited to just marketing either; serious concerns exist around the ability of insurance providers to use AI predictors to determine the cost of coverage. This further cascades into logistics issues; even assuming above-board, consensual use of patient data, regulatory bodies and compliance codes will likely need to adapt to emerging uses of data that were never dreamed about at their time of inception. Reliability and Scalability AI may be cutting edge, but so was dial up internet. We’re far from mass adoption, which means we’ll face ongoing challenges of interoperability, refinement of data quality, and more. Further, these advanced models are niche, complicated, and expensive, making them quite a way off from consumer-level adoption. Social Impact AI is still a creation of humans, some of whom are malicious, and all of whom are fallible. Bias and fairness issues can arise from AI based on the biases of those who create them, or even from ethically benign sources like poor sampling. A further consideration is interpersonal responsibility when a person is removed from the equation. For example, if AI gets it wrong, who gets served a malpractice suit? Will that impact what’s covered by insurance, or to what degree it is? These are long-term unanswered questions, but it’s crucial that industries navigate it appropriately, especially in ways that don’t pass the buck on to the consumer. What the Future Holds Despite its potential challenges, AI is likely here to stay. It’s making waves in cancer research, and its potential to take us one step closer to a cure is incredibly promising. In many ways, we have yet to scratch the surface of the technology’s potential to solve the problems of yesterday, today, and tomorrow. The advances in AI-assisted imaging and analysis of imaging like MRIs and CT-scans has a far-reaching impact that is revolutionizing how we detect cancer early. Researchers are already using AI and mathematical modeling to better understand the complex data generated from the various genomic screens and sequencing samples, and leveraging machine learning and mathematical modeling to predict therapy outcomes can accelerate therapy choices and interventions to counter side effects of treatment early. Overall, the AI revolution is poised to change the field of healthcare and cancer therapy in particular . With a heavy focus on safe adoption and ethically-friendly practices, we may be on the verge of a medical revolution as significant as the development of the first vaccines. In the meantime, the fight is far from over, and each of us can do our part to support the advancement of cancer research. How CRI is embracing the revolution. CRI is a leader in immunotherapy research, and we have long supported groundbreaking research that can help in our fight against cancer. Among many other things, this includes empowering the great minds of today and tomorrow to make the advances that help bring us closer to the finish line. This includes people like Pen Jiang, PhD, of the National Cancer Institute, CRI Technology Impact Award recipient. Dr. Jiang’s proposal will combine artificial intelligence (AI) and functional genomics approaches to potentiate cell therapies in solid tumors. The deliverables will be therapeutic gene targets to potentiate cell therapies in solid tumors, biomarkers to screen patients before treatment, and an AI framework for researchers to analyze their T cell biology data. To help support more research like this, you can join us by contributing directly to our cause here. Read more: Post navigation How Immunotherapy Extended President Jimmy Carter’s Life Read Story From Bold Resolutions to Big Breakthroughs, CRI Scientists Share Their Goals for 2025 Read Story