KEY TAKEAWAYS
- AI tools have the potential to streamline, optimise, or speed up various stages of the clinical trial process.
- This could accelerate drug development and potentially reduce the number of participants needed in clinical trials.
While advances in the field of artificial intelligence (AI) have been making headlines recently, drug development has been slowing down. Taking more than a billion dollars and a decade to complete increasingly complex clinical trials, the majority of investigational drugs never reach the market. Could AI help reverse this trend?
How can AI aid clinical research?
In a recent article for Nature, Matthew Hutson discusses how researchers have already begun to investigate the potential for AI to optimise clinical trial processes:
- Clinidigest claims to simultaneously access dozens of clinical trial records to create summaries, allowing researchers to gain a quick overview of existing trial data.
- HINT aims to predict a drug’s success during the trial design stage to ensure resources are not wasted.
- Trialpathfinder seeks to optimise eligibility criteria by testing whether broadening criteria would have any effect on risk to patients; its developers believe this would also allow for more inclusive trials.
- DQueST seeks to match patients looking to participate in research with suitable clinical trials.
- SDQ is used to extract, analyse, and clean datasets.
- Some AI tools aim to predict missing data points and identify relevant clinical subgroups.
- Others aim to monitor adherence to medication, so that investigators will not have to.
Pharmaceutical companies are now experimenting with software that completes tasks within a couple of days, which previously took 2 months.
Can AI help patients too?
As well as facilitating recruitment and including more populations in trials, AI tools could decrease the number of patients required for successful research. Hutson highlights examples such as Unlearn.AI, which aims to achieve this through creating ‘digital twins’, predicting a patient’s results had they been given a placebo. The makers claim this reduces the sample size required and allows a greater proportion of patients to take investigational drug rather than placebo.
Technologies such as ChatDoctor answer patients’ questions, which could help retain participants in clinical trials, as well as being useful in clinical practice.
Should we be worried?
As Hutson points out, there are concerns about AI and research integrity. Xiaoyan Wang, co-developer of AutoCriteria, highlights the risk of biased data and confidentiality issues when providing tools with a huge training data set. The WHO has published guidelines on making sure AI is used ethically. As is currently widely acknowledged, AI outputs may always need to be checked by a human expert.
While AI has the potential to optimise clinical trials, researchers and clinicians need to be mindful of its limitations.
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