In June 2025, Microsoft published “The Path to Medical Superintelligence,” a compelling look at what it might take to build an AI system capable of matching or exceeding expert physician judgment. While the vision is ambitious, the real challenge lies in creating AI that is not only intelligent but practical in everyday clinical scenarios.
AI in healthcare has come a long way, but not all systems are built for how care is delivered in the real world. Many are impressive in sandbox environments but struggle to keep up with the pace, complexity, and daily demands of actual clinical settings. At Counsel, we focus on building a practical medical AI for healthcare, meaning it is designed not only to analyze patient concerns and retrieve insights from evidence-based sources, but also to help clinicians guide care plans and engage patients with meaningful, personalized advice.
Key factors that define practical AI include:
Below, we explain how Counsel’s approach expands upon traditional frameworks and what makes an AI system practical for healthcare.
Many AI systems, such as Microsoft’s multi-agent framework, rely on its constituent agents to individually reason and vote on the next action. It’s a smart structure, but decision-by-committee is only as fast as the slowest agent’s response. Many simple, routine queries, like evaluating a dry cough, can take several minutes to resolve with multi-agent frameworks. That kind of delay is a non-starter in actual asynchronous care, where timely responses drive both patient satisfaction and clinical throughput.
Practical AI needs to be responsive, reliable, and built for scale.
At Counsel, we’ve built our AI systems to minimize response latency without sacrificing medical quality. Responsiveness is foundational to usability, especially in high-volume settings, so for us, latency is not only a performance metric but also a clinical requirement.
Clinical AI can’t be a tool that clinicians use outside of their existing workflows; rather, it must fit naturally into the way clinicians already work. Systems that require switching to a separate platform might work in academia, but won’t translate to busier patient care settings. Tools that live outside of an existing clinical workflow simply won’t be adopted at scale.
That’s why Counsel’s Clinician Cockpit is built as an embedded layer within our homegrown EHR. Our providers don’t need to toggle between platforms to manage patient care. Instead, all relevant patient data, such as labs, medications, imaging, symptoms, and chronic conditions, are available in a single view. After-visit notes flow directly back into the Health Information Exchange, keeping the broader care team aligned.
When AI tools integrate seamlessly into existing workflows, they become invisible helpers that support clinicians instead of disrupting them. That is what makes an AI system practical in healthcare.
Microsoft’s evaluation relies heavily on NEJM challenge cases, which are complex, rare diseases intended to test the upper bounds of diagnostic reasoning. While impressive, these cases are not representative of the questions that drive daily volume in virtual care.
In the real world, most patients aren’t presenting with a rare set of symptoms that would require lymphoma testing and bloodwork to get right. Instead, they’re reaching out about common, routine complaints: a cough that won’t go away, URI symptoms, abdominal pain, or medication side effects.
These are the types of cases we see most at Counsel, so our models are trained and evaluated on these real-world distributions. A system optimized for the NEJM challenge set may struggle to efficiently triage for these everyday workflows.
This study is innovative and a milestone in clinical LLM development, but when it comes to deploying medical AI in the real world, accuracy is only half the equation. Practicality, latency, integration, and benchmark alignment define whether a system will be used, trusted, and ultimately drive better patient outcomes.
For medical AI to truly matter, it has to work where care happens. That means being fast, intuitive, and deeply embedded in the clinical process. It means understanding common conditions just as well as complex ones and helping clinicians act confidently in both.
At Counsel Health, practicality guides everything we build. When AI is designed for real-world use, it can improve care for patients and providers alike.
Our content is created for informational purposes and should not replace professional medical care. For personalized guidance, talk to a licensed physician. Learn more about our editorial standards and review process.
