💡 Navigating the AI/ML Medical Device Landscape: Insights, Policy Shifts, and Industry Reflections

📜 A Better Reimbursement Strategy on the Horizon?

A new legislative proposal, The Health Tech Investment Act, is making waves. This bill could significantly improve reimbursement frameworks for AI-powered medical devices. If passed, it would address longstanding challenges in incentivizing innovation in AI-enabled Software as a Medical Device (SaMD).

Alan Pinstein:“Thanks for sharing, great news for all of us Imaging SaMD developers!”

💭 Clinical Acceptability in AI/ML Development: What You Need to Know

Too often, AI/ML SaMD teams get stuck in "analysis paralysis," chasing elusive perfection. Here's a quick list of hard-earned truths that could save your company millions of dollars and years of time:

• Clinical acceptability exists on a range—wide, in some cases.

• Clinicians correct AI outputs to land within that range—not necessarily to a singular "truth."

• If your AI lands within the acceptable range, that’s usually good enough.

• Measurement disagreement ≠ algorithm failure.

• Annotation protocols heavily influence standalone performance.

• Know your imaging modality’s physical limitations.

• Your device may not exactly match the predicate—that's often okay.

• FDA summaries sometimes overstate human reader agreement. Don’t panic if your device doesn’t replicate that.

By internalizing these principles, your team will be better positioned to design smarter studies and recognize when a model's performance is already clinically valid.

Naveen Agarwal, Ph.D.:“Very insightful, thanks for sharing Yujan Shrestha, MD.”

George Hattub: “Thanks for sharing, Yujan. This is truly an example of a picture saying a thousand words!”

🧠 A Landmark in Domain-Specific Generative AI: AIRead-CXR

Title: Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation Authors: Eun Kyoung Hong, MD, PhD, et al. DOI: https://hubs.ly/Q03gGDWP0

This study is a milestone in SaMD-grade AI. The AIRead-CXR model, trained on 8.8M chest radiograph-report pairs, offers a glimpse into the future of radiologic automation.

Key Takeaways:

1. Purpose-built > General-purpose AI.

2. Diverse, real-world test data boosts generalizability.

3. Sensitivity for pneumothorax hit 95.3%—impressive.

4. Human-aligned reports scored high on RADPEER metrics.

5. Outperformed GPT-4Vision significantly.

6. Less verbose, more focused than human or LLM reports.

7. Hallucination rates drastically lower than GPT-based models.

8. Clinicians preferred AIRead-CXR in 60% of evaluations.

9. Built with GMLP and 21 CFR 820 principles in mind.

10. Open access for research: https://hubs.ly/Q03gGFh50

This is what regulatory-aligned, rigorously validated AI should look like.

⚙️ From the Lab: Limits of AI Agents in Practice

AI is not ready to replace humans. I test AI agentic frameworks regularly. In my hands-on experience:

• Current models still struggle with long-term context, even with large (10M token) windows.

• Cost scales up too quickly for production use.

• Most tools today are useful assistants, not autonomous actors.

But organizations that embrace AI augmentation will win.

If you're not using OpenAI Deep Research, start now. If you're considering Manus, wait unless you can customize.

⚠️ A Potential Crisis: TCIA and the Fragility of Public Medical AI Datasets

While browsing the Cancer Imaging Archive (TCIA)—a goldmine for training and validating AI-SaMD—I saw something alarming. It's unclear if TCIA is at risk, but even the possibility is cause for concern.

This archive is foundational for AI startups. Many would not exist without it. My own first AI project used a lung nodule dataset from TCIA.

If this resource disappears, it will seriously impact innovation in medical AI.

Let’s remain vigilant about safeguarding these public data resources. The future of AI in medicine depends on them.