AI’s Transformative Impact on Cancer Care
Artificial intelligence is rapidly reshaping healthcare, bringing unprecedented breakthroughs in the fight against cancer. AI-powered innovations are accelerating drug discovery and offering new avenues for diagnostics and treatment, ushering in a period of remarkable progress in oncology. “I think we are really in the golden age of scientific discovery, powered by the cloud and AI at scale,” says Dr. Rowland Illing, Amazon Web Services’ Chief Medical Officer and Director of Global Healthcare and Nonprofits.
Cancer presents a significant challenge: the uncontrolled growth and division of rogue cells, which lack the normal genetic controls that regulate cell development. Finding ways to stop this process is where AI is demonstrating its strength, according to physicians and neuroscientists like Dr. Divya Chander. She identifies three key areas where AI is making significant strides: drug discovery, designing new therapies (such as living medicines), and precision clinical trials and precision medicine.
Accelerating Drug Discovery with AI
Traditional drug discovery is often expensive and time-consuming, but AI models are quickening the pace using the ability to identify genomic similarities across diseases. This allows them to predict a drug’s effectiveness. “It’s incredibly helpful because with older medications, we already know they’re safe,” says Chander. “When we find drugs that are useful for another indication, it’s really an extraordinary gift.” This is particularly important for rare and undiagnosed cancers, affecting over 300 million people globally.
A new open-source AI model, TxGNN, developed by Harvard Medical School researchers, is using existing medicines to find drug candidates for over 17,000 diseases, including rare cancers. Insilico Medicine, a Hong Kong-based biotechnology startup, has developed one of the first AI-discovered drugs to enter the clinical pipeline. Their Pharma.AI platform employs multiple AI models, trained on millions of data samples, to identify disease targets and design new drug compounds. These tools have sped up the process dramatically, allowing them to reach the first phase of clinical trials in just two and a half years, compared to the typical six-year timeline, and at a fraction of the cost.
“It can cost over a billion dollars per drug and take over a decade and a lot of lives are lost in that time,” adds Chander.
AI’s Role in Analyzing Complex Data
Illing and his team worked with Genomics England to handle the challenge of analyzing thousands of scientific papers on genetic links to diseases. Traditionally, this was a manual review process. “They have a team of bio curators – experts who do searches, pull out papers, read them and work out the associations between disease types and genes,” explains Illing. The sheer volume of research made it difficult to keep up.
Illing’s team developed an AI solution (automated bio-curation) that transformed this landscape. “Over an eight to 10-week period, we built a generative AI platform that was able to look at all of those papers published and identify 20 new gene associations which previously were not known,” Illing says, adding that if they had manually looked at all the data, it would be out of date. “The role of AI is very much automation. It’s how do you take stuff which needs to be done at scale globally, make it faster and take off the burden of the care providers?”
Digital Twins: Simulating Disease Progression
AI facilitates the creation of digital twins; these are sophisticated virtual simulations of disease progression. Research and Markets have projected that the digital twin industry will reach $130.77 billion by 2029, fueled by demand in healthcare and pharmacology. Dr. Eric Stahlberg, a scientist at Leidos, explained that a digital twin’s use in healthcare is developing like the use of such systems in the aerospace industry: “if you look at the aerospace industry, they’re using digital twins extensively to simulate fluid dynamics and materials processing to improve engine and aircraft designs. There are many similarities between those models and newer models that simulate the human cardiovascular and circulatory systems,” he continues. “The complexity of the human body is so substantial that there will always be uncertainty in the models. But ultimately, digital twins will inform patients, help them rank various treatment options and increase their chances of survival.”
UK Biobank is using AI to create detailed digital twins that mirror cancer development and treatment responses for individual patients. These models combine multi-modal data from genetic patterns and protein behavior to chemical changes and medical histories.
“Digital twins enable precision care in a way that could never be done before,” says Chander. “Most of the models that are coming out to date, like ChatGPT or Claude, have been language models, but we’re seeing a shift into large data models,” explains Illing. “Healthcare isn’t just language – it’s also genomics, proteins and imaging. By integrating all of those different data streams you get a much better idea, upfront, about what kind of treatment a patient can have.”
UK Biobank’s AI system analyzes both biological and clinical information to identify warning signs that traditional methods are likely to overlook. Starting with pancreatic cancer, the team is hoping to extend this to other forms of cancer.
Nanorobotics and Revolutionizing Treatment
AI is revolutionizing how doctors approach cancer treatment, allowing them to analyze, track, and target the disease with unprecedented precision, turning an uncontrollable disease into one they can analyze, track and target with unprecedented precision. One radical new approach uses nanorobotics – sophisticated molecular machines that redefine how tumors are targeted.
“These are kind of like rocket ships,” explains Dr. Divya Chander, “your rocket ship could be a bacterial cell, or it could be a virus… or it can be a nanorobot that we create, but essentially it can hold a payload. And so, the shell, the main structure, is something that can actually be activated, like a heat seeking missile.” These nanorobots, just 1 to 100 nanometers in size, act as precision delivery systems.
One example is the development of DNA nanorobots developed at Arizona State University, loaded with thrombin. These microscopic warriors seek out cancer cells and cut off their blood supply, which starves the tumors. They are used as a part of what Chander calls “living medicines”, and they are a form of targeted therapy that has fewer side effects than chemotherapy.
A new study found that nanorobots reduced tumor size by 65.2% compared to chemotherapy’s 40.5% reduction. The nanorobots also delivered drugs with 92.7% accuracy, detected tumors nearly twice as fast, and caused significantly fewer side effects. Remarkably, patients treated with nanorobots had a 78% survival rate within 12 months, compared to the 54% survival rate for patients treated with chemotherapy.
Personalized Medicine: A New Era
These AI breakthroughs are reshaping how doctors approach cancer treatment, turning an uncontrollable disease into one they can analyze, track and target with unprecedented precision. It’s a new era in medicine where cutting-edge technology can customize care. “The promise of personalized medicine is that when you come in with your specific problem, you will be treated as an individual, rather than just one of a ‘this sex, this age, in this environment’,” ends Illing. “AI is already having a massive impact on this kind of tailored understanding of what patients have got but also how things evolve over time.”
