Introducing Automate.clinic

Today, we're launching an entirely new kind of medical practice powered by our purpose-built Vitals platform. Unlike traditional medical practices where doctors treat patients, at Automate.clinic, doctors teach AI models how to think with the precision and judgment that only comes from years of clinical experience.

Our mission is simple but audacious: to make healthcare AI 100% accurate. Not 90%. Not 99%. But 100%.

The progress in healthcare AI has been remarkable, but for medicine, 90% accuracy isn't good enough. When patient lives are at stake, perfection isn't optional— it's essential. That final stretch from impressive to perfect is what Automate.clinic is designed to deliver.

By connecting doctors directly with AI development, we're creating the critical bridge between technical capability and clinical wisdom that healthcare desperately needs. This is the missing piece that will transform promising AI demos into trustworthy medical tools ready for real-world deployment.

In a healthcare environment with zero margin for error, AI needs more than technical excellence— it needs authentic clinical intelligence.

The last mile of healthcare AI

The latest AI models have achieved remarkable capabilities, appearing almost magical in their ability to generate human-like text and interpret complex inputs. But in healthcare, "almost right" isn't good enough. While 90% accuracy might be acceptable for many applications, medicine demands 99.9% precision – anything less puts patient safety at risk.

The uncomfortable truth is that training AI models on the world's content can only get you so far. Eventually, all healthcare AI will hit an accuracy wall because low-signal data isn't good enough to cross the threshold from impressive to clinically reliable. Traditional training approaches that work well for general capabilities begin to falter when confronted with the precision required for real-world medical deployment.

These past six months, I've been on a deep dive into healthcare and AI. While model accuracy is improving rapidly on standardized tests, any doctor will tell you that excelling at multiple-choice exams in medical school doesn't necessarily translate to practicing great medicine in the real world.

This brings us to a crucial question: if models are getting better at taking tests, will the same principle hold true where it's relatively easy and inexpensive to solve the first 90% of a problem, but incredibly difficult and expensive to solve the last 10%? And what role should doctors play in solving that critical final stretch?

The gap between 90% and 99.9% accuracy represents the most challenging and critical phase of development – one that requires intentional design and structured input from clinical experts with specialized experience in AI model training.

Most companies address this by hiring a handful of doctors as full-time employees, hoping this limited perspective will be sufficient to guide AI development across diverse medical scenarios and specialties. We believe there's a fundamentally better approach.

The clinical intelligence bottleneck

Healthcare AI companies today face a crucial decision: how to efficiently integrate authentic clinical expertise into their development process. The traditional approach – hiring doctors as full-time employees – comes with significant limitations:

• Limited diversity of clinical perspective: A small in-house team cannot possibly represent the breadth of medical specialization and experience needed for comprehensive model training.

• Burnout and diminishing returns: Doctors performing repetitive model training tasks all day quickly experience fatigue and disengagement, leading to declining quality over time.

• Suboptimal tools: Most companies lack specialized platforms for AI training, forcing doctors to use generic interfaces (even basic spreadsheets!) not designed for medical workflows.

• High fixed costs: Full-time medical personnel represent a significant overhead regardless of fluctuating training needs.

These challenges create a bottleneck that slows development, raises costs, and potentially compromises the clinical safety and efficacy of the resulting AI systems.

Automate.clinic: The doctor-powered solution

Automate.clinic transforms this paradigm by providing on-demand access to a diverse group of practicing doctors through a purpose-built platform designed specifically for healthcare AI development. Our approach offers several key advantages:

• Diverse clinical expertise: Access generalists or specialists across multiple disciplines, ensuring your AI is trained on authentic specialty-specific reasoning.

• Sustained engagement: Our doctors work in focused sessions that maintain high-quality output, without the fatigue that comes from repetitive full-time model training work.

• Purpose-built technology: Our Vitals platform features AI-assisted workflows and clinical reasoning capture specifically designed for medical model development.

• Variable capacity: Scale your clinical input up or down based on project needs, without the fixed overhead of full-time staff.

Two paths to clinical intelligence: The cost comparison

When healthcare AI companies are deciding how to incorporate clinical expertise into AI development, they should consider the full picture.

For the same investment you'd make in a single in-house doctor, Automate.clinic provides dramatically more flexible, diverse, and sustainable clinical intelligence for your AI development pipeline.

Why doctors choose Automate.clinic

Traditional telehealth platforms have created large networks of remote clinicians, but these doctors often report feeling isolated and disconnected from meaningful professional interaction. Automate.clinic offers them something different:

• Higher compensation than typical telehealth work

• Greater flexibility through asynchronous, task-based assignments

• Intellectual engagement with cutting-edge AI development

• Professional community connections with peers working on similar challenges

• Meaningful impact on the future of healthcare technology

For many of our doctors, working with Automate.clinic feels similar to teaching medical students and residents. Instead of explaining clinical reasoning to early-career professionals, they're guiding AI models to understand how doctors approach complex cases, identify relevant information, and make sound clinical decisions. They can identify flaws in AI thinking patterns, correct misunderstandings, and build the clinical judgment that would traditionally take years of human apprenticeship.

This teaching role offers profound intellectual satisfaction while shaping the future of healthcare technology – a combination that allows us to attract and retain exceptional clinical talent for your AI development.

Beyond model training: Capturing clinical reasoning

Our Vitals platform captures the sophisticated reasoning processes that define true clinical excellence – the decades of accumulated wisdom, pattern recognition, and nuanced decision-making that separates average clinicians from exceptional ones.

Our innovative "Rounds" assistant engages doctors in structured, case-based dialogues that mirror actual clinical reasoning, capturing not just what doctors decide, but why and how they decide it. This approach produces AI that understands medicine at a fundamentally deeper level.

Bridging the accuracy gap: from ok to great

The journey from technically impressive to clinically deployable AI requires a fundamental shift in training methodology. While foundation models can achieve impressive general capabilities from massive datasets, the final critical steps toward clinical-grade accuracy demand something different: structured, expert-driven training that targets specific failure modes and edge cases.

Automate.clinic's approach combines diverse clinical expertise with purpose-built technology to systematically identify and address the gaps preventing your models from achieving the near-perfect accuracy healthcare demands:

• Edge case exploration: Our doctor network helps identify and address the long tail of unusual but critical clinical scenarios that foundation models miss

• Specialty-specific refinement: Access to specialists across dozens of fields enables precision training for domain-specific applications

• Reasoning alignment: Our structured workflows capture not just correct outputs but the reasoning paths that lead to them, training models to think like doctors

• Error-focused improvement: Systematic approaches to identifying and remediating model weaknesses create efficient paths to dramatic accuracy improvements

This focused, expert-driven approach is what transforms "impressive demos" into "deployment-ready systems" – bridging the critical gap between foundation model capabilities and true clinical readiness.

An experimental approach to a complex problem

We don't pretend to have all the answers. The exact interface between AI models and doctors is still taking shape, and we're exploring uncharted territory. But we have a strong conviction that drives everything we're building: doctors will be absolutely mission-critical to the last mile of healthcare AI.

Our approach is intentionally experimental. We believe that the most effective way to discover what works is to build it, measure the results, and adapt quickly. We're creating a space where doctors and AI can learn from each other, with each iteration bringing us closer to the goal of truly reliable healthcare AI.

What we do know with certainty is that closing the gap between academic performance and real-world clinical reliability requires more than technical solutions alone. It demands authentic clinical intelligence – the kind that can only come from doctors who understand both the art and science of medicine.

Join us as we build the missing layer healthcare AI needs to fulfill its transformative potential.