AI Will Cure Cancer, But Not Alone
- S B
- Sep 6
- 5 min read
Cancer may be the most dreaded word in the English language. It carries with it fear and frustration, anger and ambiguity, sorrow and suffering, terror and tears.
There is not one cancer, but many. And so we are not searching for a single cure, but for many cures.
Although cancers develop and spread through similar mechanisms, the paths to effective treatments vary greatly. This is precisely where artificial intelligence (AI) can help us. This is where AI will help us.
For decades, we have raised funding, built cancer centers, and improved treatments. But the cures have eluded us.
We walk for miles and miles. We donate at cashier counters. We do our part. Now AI must also do its part.
The Promise and the Skepticism of AI
The promise of AI is grand. But if you are skeptical, your lack of enthusiasm is justified. This is not the first time a technological revolution has promised cures for human disease.
The Human Genome Project was supposed to take us to new frontiers in medicine. It was supposed to save us. There were promises of personalized therapies and cures for genetic diseases like cystic fibrosis and sickle cell. Yet, those diseases remain uncured. Public literacy around genetics remains minimal. In fact, the most visible consumer outcome was 23andMe, which allowed individuals to access parts of their genetic story. That company has now gone bankrupt.
This is not to say the Human Genome Project did not move the needle. It did. But it did not deliver on its most ambitious promises. AI now stands in its place.
Why I Believe AI Will Be Different
AI has been part of medical research for decades. But modern AI, specifically large language models (LLMs), has ushered in a new era. We have renewed vigor and perhaps hope that this time the technology will deliver.
I believe it can. I believe AI can cure cancer.
My belief is not mere evangelism. As a graduate student, I used machine learning, a type of AI, to predict Ebola virus mutations, identifying vulnerable targets for vaccines. AI could analyze thousands of viral sequences in hours, a task that would have taken human scientists years. That work showed me AI's potential to solve real problems. Not just to boost productivity, but to save lives.
For many years, the greatest barrier to realizing AI's potential was cost. With technology companies like Google and Amazon investing in AI for health research, we have a unique opportunity where cost is no longer the greatest barrier. In the traditional research model, scientists compete for limited grants, often funded by taxpayer dollars. There is no bottomless pit of funding. When tech giants enter the conversation, the funding challenges fall away, though not without creating new problems. This introduces ethical and systemic challenges, from protecting patient data to ensuring research findings are shared openly and fairly.
The ask is great. Can AI cure cancer?
At the surface, the answer is yes based on what we see today. AI can accelerate drug discovery by optimizing clinical trials and helping scientists develop new therapies using genetic information. AI can also aid in the early detection of cancers. All of these speak to the ability of AI to rapidly analyze massive amounts of data.
But is speed all we need to get to the finish line? No. We need to slow down and ask the right questions. The first step in the scientific method is, after all, observation.
AI can help us observe. AI can help us ask the right questions, collect the right data, analyze it, and help us draw the right conclusions.
This is the power of modern AI. It doesn't just do. It simulates thinking.
By accelerating discovery and supporting human reasoning, AI can enable unprecedented progress. But only if we approach it with the right mindset.
The Risk of False Hope
When Google announced its AI Scientist, many people were curious, myself included. It is indeed a remarkable achievement, an AI that can assist with scientific research.
But announcements like these, though earned, often create false hope in the general population about how close we really are to curing human diseases, especially cancer.
These are task-based tools that accelerate current ways of working. They make it easier to analyze data and generate hypotheses based on existing paradigms and datasets. But science requires original thought.
Along the same line are the studies that compare doctors to AI in tasks like making diagnoses. Headlines proclaim that AI has outperformed doctors by a certain percentage.
Again, while we should not minimize these achievements, we must be careful about the message we send. AI is not replacing doctors anytime soon. In fact, the positioning should and will be the opposite: AI will work alongside medical professionals to treat patients.
Returning to Google's AI Scientist, the reality is the same. AI is not a replacement. It will be a partner. Why does this messaging matter?
It matters because the public must understand that we still need new ideas and new minds. In other words, we still need young people to pursue careers in medicine and research.
From Promise to Purpose
We have the funding. We have the interest. We have the technology. What we need are the people.
The next great frontier in medicine and research will not be AI-driven or human-driven. It will be human-led and AI-powered.
While copilots have been handy, AI images beautiful, and AI videos entertaining, we still need AI where it matters most.
AI can cure cancer, but not alone. It will require more than keeping 'humans in the loop.' It will require humans and AI working side by side.
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"AI is the tool, but the vision is human." — Sophia B.
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About the Author
Sophia Banton works at the intersection of AI strategy, communication, and human impact. With a background in bioinformatics, public health, and data science, she brings a grounded, cross-disciplinary perspective to the adoption of emerging technologies.
Beyond technical applications, she explores GenAI’s creative potential through storytelling and short-form video, using experimentation to understand how generative models are reshaping narrative, communication, and visual expression.
