Data management
Using clinical trial matching, AI can automatically identify eligible patients and match them with suitable trials.
is involved in new research using wearable sensors to track the movement of people with Duchenne muscular dystrophy and Friedreich’s ataxia. Both are rare degenerative conditions that hinder movement and can eventually lead to paralysis. Faisal and his team used data collected from participants with such conditions and healthy controls to identify specific movements, which they called “behavioural fingerprints”, that were illustrative of someone’s physical capabilities. For example, how well they could make a circle with their hips, as if using a hula hoop.
“In future, because of the precision of this [AI] system, we’ll also need much fewer patients [in trials].”
Aldo Faisal 90%
The percentage of clinical trials that fail BIO Industry Analysis
42
The team were able to define behavioural fingerprints that distinguished people with the disease from the controls, and then fed this insight into machine learning algorithms to predict how the conditions would progress up to 12 months later – which they could do with greater accuracy than clinical scales considered the gold standard, says Faisal. “In future, because of the precision of this system, we’ll also need much fewer patients [in trials],” he adds. This could be a boon to trials for rare diseases, where smaller patient populations make recruitment even harder.
Preventing dropout
The same type of AI used within digital disease diaries can also predict whether a trial is at risk
of failing due to patients backing out. “Filling spots of dropouts is a massive problem and actually one of the major sources of failure of trials,” Harrer says. “It carries exponential costs, even more than if you’re recruiting into the trial in the first place.” Some trial designers report planning for dropout rates as high as 30%. AI can identify behaviour that suggests a patient isn’t following protocol, giving trial staff a chance to intervene and correct the problem. “You want to pick up and maybe even predict these patterns that point to behavioural changes,” explains Harrer. “For example, patient A is always missing that pill on a Tuesday morning, because they have to take their kid to school.” This could be as simple as having a sensor that fires every time a pill case is opened, or having an accelerometer in a smartwatch, he adds. Machine learning algorithms could then dig through and recognise patterns in all that information. In addition, another use of AI is through chatbots to send prompts to help re-engage patients. For example, if the model knew a patient usually forgot to take their pill every Thursday because they were so exhausted after work, it could send a reminder to go for a walk so they’d feel more energised and be more likely to remember. “AI-powered coaches can interact with patients and caregivers, and raise the alarm, but also come up with intelligent intervention strategies to prevent dropout,” says Harrer. “Because you can’t expect a clinician to call up every patient that’s supposed to take a pill at five o’clock.”
The human touch
But for all the capabilities that AI brings, it’s important to remember that it should be used to complement, rather than replace, human clinical expertise. “Humans must never be taken out of the loop,” says Harrer. “AI must always be used as an assistive tool.” AI lacks empathy and can’t make ethical judgements; it sees patients only as data points. Where AI shines is in helping clinicians and trial staff do their jobs better: retrieving knowledge, collecting new and more precise data, and sorting through information much faster than we can. Harrer expects that in the coming years, we’ll see more solutions cropping up in relation to digital twins, AI chatbots for patient coaching, and clinical trial matching. For Faisal, having AI in a clinician’s arsenal means they could investigate a wider range of conditions. Currently, there are over 6,000 rare diseases, but most have no treatment because it’s not economically viable to develop them, he stresses. “But if you can take the risk out, and make it faster and cheaper, then we will see a lot more treatments coming up.” ●
Clinical Trials Insight /
www.worldpharmaceuticals.net
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