601 - How Artificial Intelligence Is Influencing the Way Healthcare Software Is Built

Why It Matters

AI-driven acceleration of development can shorten time‑to‑market for critical health tech while raising compliance and skill challenges.

Key Takeaways

• AI accelerates code generation, reducing development cycles dramatically
• Early cloud-native architecture enables scalable, real-time clinical data flow
• Prompt and context engineering become essential skills for large codebases
• AI tools help explore multiple design options quickly for UI/UX
• Balancing AI assistance with regulatory compliance remains a critical challenge

Summary

The podcast features Sean Walker, CTO of Cidian, discussing how artificial intelligence is reshaping the way healthcare software is built.

Walker explains that a cloud‑native, real‑time data platform was designed from the start to support machine‑learning models, allowing the company to scale across multiple hospitals. He highlights that AI tools now generate detailed business requirements and even draft code, cutting weeks of manual effort. Prompt engineering and, more importantly, context engineering have become core competencies for engineers working on a decade‑old codebase.

Walker humorously describes his “AI disease,” staying up 18‑hour days to integrate generative models, and likens AI to adding extra engineers that unblock development. He notes that AI agents can automate routine tasks and that exploring several UI designs or architectural options can be done in minutes rather than days.

For healthcare vendors, these advances promise faster delivery of new clinical features, improved patient insights, and the ability to compete with tech giants. However, they also demand new talent, rigorous governance, and careful alignment with regulatory requirements.

Original Description

In this episode of Talking HealthTech, Peter Birch speaks with Sean Walker, CTO of Alcidion, about the role of AI in healthcare software development, the architecture behind Alcidion’s Miya Precision platform, and how thoughtful AI adoption is helping improve outcomes for both clinicians and patients.

The conversation explores how healthcare software is evolving, from the importance of getting foundational architecture right through to building systems that can scale and adapt as clinical needs change.

They also unpack how AI is being used in practice, not just as a clinical support tool, but as a way to accelerate development, streamline workflows, and enhance the way software is designed and delivered.

Along the way, the discussion highlights the balance between speed and responsibility, the growing role of regulation, and why keeping clinicians involved remains critical to building trust in AI-driven systems.

This episode offers a grounded look at where AI is making a real impact today, and what it takes to build healthcare technology that is both innovative and safe.

Key Takeaways

🚀 Building strong software foundations is essential for scaling and integrating advanced capabilities like AI and large language models.

💡 Distinct uses of AI exist: both in software development processes and as clinical decision support tools.

👥 Keeping humans in the loop is critical for maintaining trust and reliability in clinical AI applications.

🛠️ Skills like context engineering and agentic workflows are becoming necessary, beyond simple prompt engineering.

🌐 Registered and codified AI tools, such as Alcidion’s concept detection, enable more robust clinical data and contribute to improved patient outcomes.

Timestamps

01:12 — Introduction to Alcidion and Sean’s role

03:06 — Approaching platform architecture in healthtech

06:30 — Disruptive AI capabilities and transformations

10:22 — AI’s impact on software development workflow

19:49 — Outcomes for clinicians and patients

23:48 — TGA certification and concept detection

28:09 — Human-in-the-loop for clinical AI

34:08 — Future-ready software developer skills

Check out the episode and full show notes on the Talking HealthTech website.

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