As we mark Rare Disease Day 2026, it is worth reflecting on the developments in digitalisation, artificial intelligence (AI), and other technology in healthcare over the past year, and their role in shaping how rare disease clinical trials are steadily improving patient experience and helping to bring new treatments to market faster.
Rare disease clinical trials face a number of unique challenges due to factors such as small and dispersed patient populations, and the ethical implications of traditional placebo control arms for potentially life-saving treatments, among others. These factors mean that traditional clinical trial protocols that may work for some indications do not translate for the unique needs of rare disease clinical trials.
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The challenges
What makes a trial efficient can be defined in various ways, but key criteria that are particularly relevant are recruiting the right patients, recruiting them faster and then retaining them. Inadequacy of these components of a trial risks researchers collecting incomplete or inaccurate results and thus holding back progress in finding treatments, as well as being detrimental to the patient experience and wellbeing in trials.
Let’s first look at enrolment. While it is estimated that there are over 300 million people living with rare conditions, patient populations are often disparate. Targeted recruitment is therefore much more difficult and labour-intensive than for some other types of clinical trials.
There is an urgency to solving the recruitment gap. Around 95% of rare diseases have no approved treatment options, and clinical trials are often the only viable treatment. Since many patients view clinical trials as a means to receive treatment, a lack of reach in the recruitment process can mean disparities in available care.
Secondly, once they are in fact enrolled, it is the end-to-end patient experience that will determine their likelihood to stay. Rare disease trials can be more demanding for patients and this can lead to drop-outs, not to mention a huge personal toll on trial participants.
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By GlobalDataConsenting processes, still largely paper-based, are complex and overwhelming. Traditional on-site trial interactions are time-intensive and financially burdensome for those who must travel long distances, which is often inevitable for many rare disease patients due to the scattered nature of patient populations. Burdensome symptom reporting diaries still exist but thankfully are declining.
Separately, the need for control arms in clinical trials can be particularly unattractive for rare disease trial participants. Traditional study designs commonly require one-third to one-half of trial participants be assigned to receive a placebo or the current standard of care treatment. Patients are sometimes disappointed by this assignment since they may have the view that the investigational therapy is a superior option, especially for genetically targeted therapies, for example. Early discontinuation from the trial after assignment to the control therapy can be common and can threaten the interpretability of the trial results.
Due to the very limited availability of treatment for rare diseases and an estimated 85%-90% of rare disease cases being serious or life-threatening, use of placebo or inadequate control therapy raises serious ethical concerns for rare disease trials.
Synthetic control arms
To tackle these issues associated with placebo control arms, synthetic control arms (SCAs) through Virtual Twin experiences can be used to compare trial treatment data.
These SCAs provide a robust, regulatory-grade alternative to traditional clinical trial designs, offering significant impact within rare or biomarker-driven populations where unmet medical need and patient recruitment are persistent barriers to progress. They are formed out of existing historical data from previous trials, as well as sources such as real-world data and registry data. Regulatory bodies such as the US Food and Drug Administration (FDA) and EMA are developing guidelines around their use, and research has shown that they can offer the same quality as typical randomised control arms.
SCAs can reduce the overall number of prospective trial participants, especially helpful where there may not be a large population available for a given rare disease. In addition, a larger portion of patients enrolled in a rare disease trial will be able to take the study drug.
SCAs make the trial process more efficient and more informative, speeding up recruitment and increasing retention ultimately allowing patients to gain access to life-changing treatments faster. With 70% of rare diseases beginning in childhood and 1 in 3 children with a rare disease who won’t live to see their fifth birthday, it is pressing to get the right people the right treatments. SCAs can also be used to help design trials and improve the efficiency of clinical development programmes. They create dynamic, real-time representations of trials, healthcare ecosystems and can improve the patient experience. For example, we have worked on trials where a sponsor was able to use an SCA to estimate a treatment effect size early in development and ultimately reduce the size of their Phase II trial. This illustrates the financial and time impact that using SCAs in the early stages of clinical development can have.
The role of technology
As every industry strives to digitalise and adopt trending technologies such as AI, how can clinical trials adapt and innovate in ways that meaningfully address the barriers for those affected by rare diseases? Below are just a few emerging and established technologies that have been revolutionising rare disease clinical trials by simply focusing on the patient experience.
Decentralisation
Digital tools that enable hybrid trials are one way to ease the financial and personal cost of participating in a clinical trial whilst maintaining data collection standards.
The adoption of tools such as wearable health trackers, or even simple changes such as scheduling video calls rather than face-to-face consultations with a clinician when a physical examination is not needed, can massively help with patient retention, as they are no longer encumbered by long travel distances.
As well as improving patient retention, decentralisation plays a key role in widening access and improving patient enrolment. When a trial includes remote elements, potential participants are less disadvantaged by their distance from the trial site, and the decision to take part can be a much easier one.
AI in trial planning
AI has emerged as a valuable tool for improving the efficacy of patient enrolment.
AI and predictive analytics of multiple large, external data sets from different sources are an emerging use case of AI that enables trial planners to identify high priority sites to target recruitment. This allows rare disease clinical trials to more efficiently and accurately reach those who are most suitable for enrolment based on existing data.
Steady progress is being made
While it is important to celebrate the achievements of technology in the past few years in addressing some of the key barriers for rare disease patients in clinical trials and innovating the patient experience, it is equally as important to carefully evaluate each implementation and recognise where there is still the need for improvement.
For example, many new technologies being implemented in clinical trials rely on the availability of data. It is imperative, therefore, that the data patients volunteer to provide and we collect as researchers is shared and used to the largest extent possible, to advance all aspects of effective treatments and not limit its use to support the conclusions of just one trial.
We have seen that clinical trials are adapting sensibly, balancing the adoption of tools that have potential to address urgent rare disease patient needs with responsible trial practices.
As we look to next year, there is potential for both existing and new technology to revolutionise aspects of clinical trials for rare diseases. Amid the conversations around AI, it will be fascinating to see how the technology is strategically improved and integrated. We expect continued growth in SCAs in addition to new advances using predictive modelling to better design and analyse rare disease trials. It’s promising to see such an indispensable industry lead in the end-to-end digitalisation and innovation in trials for the benefit of what has historically been a sometimes overlooked population.
