The likelihood is that you've encountered content related to ChatGPT in the previous six months. If your experience on LinkedIn resembles mine, you likely find it challenging to scroll for a minute without encountering posts touting the transformative potential of generative AI in healthcare. And truth be told, some of the excitement is justified.
AI in Healthcare
Imagine employing AI to compose your trial protocol, granting your clinical teams invaluable time to focus on higher-level tasks. Alternatively, envision AI automatically refining continuous streams of data from various sources, thereby freeing up resources on the data engineering front to create more extensive integrations with real-world data, bridging the gap between clinical development and commercial applications.
AI could even function as the investigational product itself, serving as a digital coach or an element within a combination product.
The possibilities for utilizing AI in clinical research are boundless. What may not be immediately apparent is that these examples aren’t merely futuristic aspirations; they represent the current landscape.
AI is not only present today but is also an enduring facet of our reality. From the escalating healthcare expenses in America to the shortage of tens of thousands of physicians and the prevailing economic challenges causing turbulence in the field, abstaining from utilizing these tools is a luxury we cannot afford. Instead of questioning why, we should be pondering why not.
However, this raises a pertinent query: Why haven’t we witnessed a more widespread influence, adoption, and value of AI in clinical research?
Additionally, industry-wide progress has materialized through collaborative endeavors. Over the past year, the Coalition for Health AI (CHAI) has unveiled an initial draft blueprint for reliable AI implementation guidance and assurance in healthcare. It has also developed numerous frameworks that delve into aspects of credible, fair, and transparent health AI systems.
The Digital Medicine Society (DiMe) has released toolkits to assist AI developers in navigating the regulatory landscape at the U.S. Partnerships at the intersection of healthcare and technology, such as the recent collaboration announced by AWS and Eversana, persistently emerge, charting a course for pharmaceuticals, CROs, product developers, and other stakeholders in our domain.
However, these guidelines, frameworks, and toolkits are specifically tailored to aid innovators at the forefront, where precedents are lacking or the application of regulations remains ambiguous. Not all AI is crafted equal. There are other forms of AI in our industry today. Why haven't we reaped the benefits of these tools? Why haven't they been widely adopted?
Are we truly equipped to extract the utmost potential from AI? Frankly, we are inclined to believe otherwise.
A statistic shared earlier this year by the Tufts Center for the Study of Drug Development states that the average duration for a single organization to transition from the pilot phase to the implementation phase is six years. Furthermore, beyond that, the average time required for our field to achieve full-scale implementation (as standard practice) amounts to 20 years.
Considering this, perhaps a more pertinent question we should be posing today is: How can we prepare for the widespread adoption and scalability of AI—when AI becomes the norm in our operations? We believe the answer comprises three components.
How to make your Organization AI-ready
- Firstly, we must persist in pioneering progress. Those of us at the forefront must continue to pose challenging inquiries and conduct research to ascertain the most effective ways of leveraging these technologies to enhance the vital work conducted by each individual in their day-to-day operations, ensuring it is ethical, efficient, inclusive, and safe.
- Secondly, our collective progress must persist. Regulators cannot draft guidelines for gaps they are unaware of. Companies can only capitalize on new benefits if they are aware of their existence. Engaging in collaborative efforts and discussions across multiple disciplines before competition becomes vital to establishing comprehensive best practices for current and future tools.
- Third, education is really important. It is more than just reading an article on LinkedIn. Learn about AI frameworks to better understand the viewpoints of leaders at the frontlines of these conversations. Use current tools to improve your skill set, allowing you to identify reputable items and partners among the hype.
In Summation
Perhaps this path isn't as simple as a few minutes of LinkedIn every day. Nonetheless, in the context of the people we are committed to helping, planning for the possibilities and allowing faster access to life-saving medications is a time investment well spent.
Want to know more about the full extent of our AI-powered eClinical software solutions? You can have a quick chat with one of our experts by following this Link. We look forward to hearing from you. Watch this space for more information, updates, and fresh insights for your clinical trials in Octalsoft’s vast library of scientifically driven publications by our team and industry key opinion leaders.
