Before you roll your eyes in digital disbelief, ClinicalLeader.com estimates that AI will be utilized in 60-70% of clinical trials by 2030, cutting the biopharma industry $20-30 billion annually due to quicker trial timelines and reduced trial sizes. Although AI digital twins may represent only part of this expansion, the capability to virtually assess the safety and effectiveness of new medications would mark a substantial advancement for pharmaceutical companies, healthcare providers, and patients alike.

AI Digital Patient Twins Are Here – But What Are They?

It's crucial to recognize that AI-created, patient-specific digital twins aren't science fiction—they're here now. Digital patient twins are currently under evaluation by regulatory bodies and quietly incorporated into trial designs throughout the industry.

Simply put, a digital twin in healthcare is a computer-generated replica of an actual patient that forecasts how that person's health might evolve over time without receiving the new treatment. It’s constructed using previous medical records and aids researchers in comparing what actually happens to a patient with what likely would have occurred if they received the standard treatment or a placebo. This is already transforming the field.

"Our AI-generated digital twins are comprehensive, personalized predictions of a patient’s future health outcomes based on their baseline characteristics. These predictions cover the wide range of assessments, lab tests, and clinical events that define a patient’s health journey," said Aaron Smith, founder and machine learning scientist at Unlearn.ai, via email.

"Digital twins are potent tools to comprehend disease progression and address key clinical research inquiries—one of the most vital being how to make clinical trials more effective."

How AI Digital Twins Reduce Clinical Expenses And Time

Unlearn is among the leading firms leveraging digital twins to enhance trial efficiency. Their proprietary "Digital Twin Generators" employ disease-specific neural networks to predict how a patient's condition would progress over time without the new treatment.

This introduces two primary applications: first, as simulated controls in single-arm clinical trials, and second, as prognostic covariates—basically, a set of details about a patient to forecast health outcomes—in randomized controlled trials.

Both approaches are gaining approval from regulators, including the FDA and European Medicines Agency. The EMA has already issued a formal qualification opinion for Unlearn’s PROCOVA method—an advanced form of covariate adjustment that relies on digital twin data to boost trial power without enlarging the sample size.

"In randomized controlled trials, digital twins augment the data collected in both the treatment and control groups, enhancing statistical power without compromising statistical rigor," Smith clarified. "Alternatively, digital twins can decrease the number of control participants required while preserving the original power, making studies smaller, faster, and more efficient."

The ramifications are considerable. According to Smith, even a 10% reduction in sample size for a Phase 3 trial could shave four months off enrollment time and save tens of millions of dollars. For patients with rare diseases or aggressive cancers, those months are significant.

AI Digital Twins As A Compassionate Alternative

The digital twin technology is particularly appealing in scenarios where traditional control groups are challenging or ethically complex—think pediatric trials, late-stage oncology, or ultra-rare conditions. However, in each of these instances, it wouldn't be appropriate to withhold potentially life-saving medications from patients in the control arm of the study.

Most control group participants are administered placebos—without their awareness or the clinical investigators'—to establish a dependable baseline for the actual medication to outperform. For such delicate ethical issues, digital twins offer a realistic and regulator-approved alternative to real-world control arms.

Smith pointed out a common misunderstanding regarding the AI technology, which assumes that digital twins function solely as synthetic controls. "While that is one valid application, a wholly separate and arguably more scalable application is the use of digital twins in randomized controlled trials," he stated.

"In that scenario, digital twins don’t serve as substitutes for patients. Instead, they supply additional prognostic data about each enrolled participant."

This extra layer of data refines findings and makes trial outcomes more dependable. Yet, it also raises questions concerning transparency and explainability—concerns that regulators haven't disregarded.

"We prioritize transparency, detailing every facet of our models—from how they’re trained to how they’re validated in their specific context of use," Smith remarked. "That trust is pivotal to adoption."

The clear advantage of AI digital twins is evident.

Considering rising costs, delays, and participant recruitment challenges, digital twins could alleviate pressure across the clinical research sector. A peer-reviewed position paper published by Unlearn noted that the utilization of digital twins could lessen the strain on actual trial participants by decreasing placebo assignments and shortening overall trial duration. This isn't merely a technical enhancement—it's a humanitarian one.

Nonetheless, digital twins are only as reliable as the data that trains them, and healthcare datasets remain fragmented and cluttered. Bias, inaccuracies, and missing values can all impair model dependability. Thus, Unlearn underscores stringent validation within each trial's specific context. "Validation in the context of use is an essential step in applying these methods," Smith emphasized.

Looking forward, expanding the use of digital twins necessitates more than AI innovation—it demands a cultural shift. Trial sponsors, clinical research organizations, and regulators must become accustomed to machine-learning outputs influencing critical clinical decisions. But if recent history serves as a guide, this transition is already underway.

"We're at a turning point," Smith stated. "Digital twins are no longer hypothetical. They're operational, trusted, and delivering value today."

And for an industry perpetually seeking smarter, faster, and safer ways to deliver treatments, AI's progression toward digital twins could be precisely what the doctor ordered.

ForbesAI Can’t Fix Clinical Trials Without The Right People, New Parexel ReportBy Tor Constantino, MBA

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