Clinical AI Innovators Network – July Reflections
Last week’s Clinical AI Innovators Network meeting was another energizing milestone. We explored timely topics ranging from how to choose the right AI tool versus model for a given task to the surprising realism of today’s voice-based AI features. These voice demos were a clear highlight—several members noted how seamless and human-like the experience has become, opening up possibilities for use cases like hands-free note capture, real-time stakeholder communication and brainstorming.
We also discussed the creative potential of image generation models and the importance of remaining vigilant about bias in their outputs. Without intentional prompting, many images reflect default patterns that can reinforce stereotypes. This sparked a thoughtful dialogue on the responsibility that comes with using these tools in clinical research.
Beyond the tools and demos, the most rewarding part of this network is the community itself. It's been a pleasure to reconnect with past colleagues and to meet new professionals who are equally excited about the role of AI in clinical development. Interestingly, we’re about 50% AI novices—those looking to build fluency and confidence in basic AI tools—and 50% intermediate to advanced users who are already experimenting with deeper integration into their daily work.
For those of us in that latter group, the learning never stops. We're planning to discuss how to chain GPTs together for more advanced workflows, how to apply multi-agent coordination, and how to improve model performance by using creative debugging techniques.
This week alone, I learned a new troubleshooting method from a podcast that’s already improved how I refine my GPTs:
Ask ChatGPT to convert the configuration into Python code.
Paste that code into Claude for debugging support.
Paste the debugged version back into ChatGPT for execution or refinement.
I’ve also begun cross-validating outputs across multiple models—ChatGPT, Copilot, Gemini, Grok, and Claude—especially when the stakes are high or the prompt complexity increases. By asking one model to critique or fact-check the output of another, I’ve seen a notable improvement in accuracy, nuance, and overall reliability.
The big picture: Why this matters
If there’s one thing this community reinforces, it’s this: everything is changing fast. The AI tools available to us today are already dramatically more capable than they were just months ago. Staying current isn’t just a technical necessity—it’s a strategic imperative.
That’s why this network has become such a valuable space. For some, it's about learning how to start using AI tools confidently. For others, it's about pushing the edge—figuring out how to scale, validate, and govern agentic workflows responsibly. For all of us, it’s about asking better questions, testing assumptions, and leveling up together.
As we look ahead to topics like digital twins in clinical trials, one thing is clear: the future of drug development will be shaped by those who are both curious and adaptable.