How Artificial Intelligence is Shaping the Future of Precision Oncology

Artificial intelligence (AI) has moved beyond the realm of science fiction and into the daily lexicon of modern medicine. For Chadi Nabhan, MD, MBA, FACP, hematologist-oncologist and the chief medical officer and head of strategy at Ryght.AI, the technology’s integration into oncology is not just inevitable: It is happening at a breakneck pace.

"I think it's fair to say that we hear about AI every day, probably every minute, if not every second," Nabhan observed in an interview with Targeted Oncology. "It’s clear that AI is going to be part of how we approach health care broadly and cancer care specifically, because it's an innovative approach, innovative technology."

Redefining Precision Oncology Through AI

Precision oncology represents a fundamental shift in how we treat malignancy. Rather than the broad-spectrum "carpet bombing" of traditional chemotherapy, the field focuses on "identifying a gene, a target, a biomarker, that we can develop a drug against, that hopefully can provide more benefit to the patient and less toxicity."

Nabhan explained that this approach aims to attack the cancer cells specifically while sparing normal cells. His current mission is to bridge the gap between AI—a tool often viewed as the domain of software developers—and the practical needs of oncologists.

"Let's be honest, we're not really techies. We're not software developers, so we know enough about [AI] to be dangerous in a conversation... but let’s imagine how could approaching precision oncology be in the era of [AI],” he said.

Practical Applications in R&D and Clinical Settings

Today, AI is being applied across the "precision oncology ecosystem."For pharmaceutical and biotech sponsors, the technology is revolutionizing research and development (R&D) and target screening. One of the most significant breakthroughs cited by Nabhan is AlphaFold2, an AI platform that allows researchers to better understand the folding of proteins. This is critical for designing small molecules against specific targets.¹

"The scientists who discovered or who created the AlphaFold, they won the Nobel Prize for Chemistry in 2024," Nabhan noted, highlighting the technology's validated impact.¹ Beyond molecular biology, AI is tackling administrative bottlenecks. "If it's going to take you 6 months to write an informed consent…can you cut that into a week using AI? In my view, this is only scratching the surface."

In the clinic, AI is manifesting as "ambient AI" and predictive analytics, including electronic scribes, predictive analytics to anticipate patient complications, and communication support. Nabhan notes the latter can be especially helpful for young physicians to navigate difficult conversations they may not have encountered, like end-of-life discussions, using large language models to "craft the best dialog and conversation how to convey the message."

Navigating the Risks: Bias, Liability, and Regulation

Despite the innovation, Nabhan was candid about the challenges. "Nothing really is always perfect," he remarked, identifying 3 primary hurdles:

1. Bias: Output depends entirely on training data. If an algorithm is trained on data from older White men, its applicability to younger Black women may be compromised.
2. Liability: The legal landscape remains murky regarding AI hallucinations. If a physician uses AI to make a clinical decision that leads to an adverse outcome, who is liable—the physician, the AI developer, or both?
3. Regulation: There is no consensus on who should regulate these tools. Nabhan argues for a "combined private public sector" entity to ensure AI is used in "patient-friendly fashions."

The Evolution of Clinical Trials

To Nabhan, one of the most promising applications of AI involves shrinking the timeline for clinical trials. Currently, trials can take 10 to 15 years and cost between $1 billion and $3 billion.

"We must really start thinking, how can AI help shrink this?" he asked. "The reason I'm very passionate about this because it's all about timing... Imagine if we had these drugs sooner, there would have been patients' lives that are saved."

One solution is a digital site research twin, an AI-powered model of clinical research sites.² By knowing exactly what a site can do qualitatively and quantitatively, sponsors can select the precise sites where a trial is most likely to enroll patients quickly. With roughly 15,000 oncology trials currently active—over 40% of which are precision oncology trials—the ability for AI to "come to the rescue" and bring drugs to market faster is more than a technological goal; it is a clinical necessity.

REFERENCES

1. Nobel Prize in Chemistry 2024. The Nobel Prize. October 9, 2024. Accessed February 18, 2026. https://tinyurl.com/5wdrfs5s

2. How AI Site Twins are revolutionizing clinical trial timelines. Ryght.AI. Accessed February 18, 2026. https://www.ryght.ai/ai-site-twins-ryght-ai