There’s a lot of discussion right now around AI in healthcare, but most of it feels disconnected from how clinical research actually works day-to-day.
A lot of the conversation is centered around automation, replacing people, chatbots, and efficiency. Some of that matters, but one thing I think about much more is whether AI could eventually lower some of the barriers that have historically limited access to clinical research and life-saving treatments in the first place.
I’ve worked in clinical trial technology for over 10 years and spend most of my time talking to sponsors, CROs, data managers, and operational teams running studies. One thing that has always stood out to me is the disconnect between who participates in clinical research and who ultimately benefits from it.
Low-income and underserved communities in the U.S. often bear a disproportionate share of early clinical research participation, yet frequently do not receive equitable access to the resulting drugs or therapies once they reach the market. In many cases, the same populations helping generate the data behind new therapies may never realistically be able to afford them afterward.
None of this magically fixes healthcare inequality. Drug pricing, insurance systems, and broader socioeconomic issues are obviously much larger than AI. But I do think AI could help reduce some of the operational and financial barriers that make clinical research so concentrated within large institutions and wealthier healthcare systems today.
Clinical research is still incredibly expensive and inefficient in a lot of ways. Study startup takes too long. Protocols are getting more complicated. There is still an enormous amount of manual work involved in data review, reconciliation, monitoring, and study management. Smaller biotech companies and research organizations often struggle just to get studies operational.
This is where AI could realistically help.
If AI can reduce some of the time and manpower required for things like study configuration, protocol review, patient identification, monitoring workflows, and operational oversight, that could gradually lower the cost and complexity of running clinical research. That may create more opportunities for smaller organizations, universities, nonprofits, and emerging biotech companies to pursue research that historically may not have been financially realistic.
I also think AI could help decentralize parts of clinical research infrastructure that have traditionally been concentrated in major academic centers and large healthcare systems.
A lot of patients are excluded from trials simply because of geography and access. They may live too far from research sites, not have reliable transportation, work jobs that make repeated site visits difficult, or never even hear about relevant studies. AI combined with decentralized trial technologies could help identify eligible patients earlier, support remote participation models, and reduce some of the logistical barriers that keep people out of research entirely.
Another issue is that healthcare and clinical research data is still extremely fragmented. A huge amount of potentially useful information remains buried in PDFs, scanned records, disconnected systems, and unstructured physician notes. Historically, most smaller organizations simply didn’t have the resources to work with this type of data at scale. AI is starting to change that.
At the same time, there’s also a real risk that AI could make some of these disparities worse if it’s implemented irresponsibly. If models are trained on biased datasets, deployed only within wealthier healthcare systems, or used without enough human oversight, the technology could easily reinforce the same inequities that already exist today.
I don’t think AI is going to “solve” healthcare inequality. But I do think it has the potential to gradually reduce some of the operational friction, infrastructure barriers, and resource limitations that have historically limited who gets to participate in research and who ultimately benefits from medical innovation.
That’s probably the more realistic way to think about it.
Author Bio
Amanda McLean, VP of Sales & Customer Enablement, ClinCapture
Amanda McLean is VP of Sales & Customer Enablement at ClinCapture, an eClinical software company focused on EDC and decentralized clinical trial technology.
