This is the new reality for patients at a small number of clinics in Southern California that are run by the medical startup Akido Labs. These patients—some of whom are on Medicaid—can access specialist appointments on short notice, a privilege typically only afforded to the wealthy few who patronize concierge clinics. The key difference is that Akido patients spend relatively little time, or even no time at all, with their doctors. Instead, they see a medical assistant, who can lend a sympathetic ear but has limited clinical training. The job of formulating diagnoses and concocting a treatment plan is done by a proprietary, LLM-based system called ScopeAI that transcribes and analyzes the dialogue between patient and assistant. A doctor then approves, or corrects, the AI system’s recommendations. “Our focus is really on what we can do to pull the doctor out of the visit,” says Jared Goodner, Akido’s CTO. According to Prashant Samant, Akido’s CEO, this approach allows doctors to see four to five times as many patients as they could previously. There’s good reason to want doctors to be much more productive. Americans are getting older and sicker, and many struggle to access adequate health care. The pending 15% reduction in federal funding for Medicaid will only make the situation worse. But experts aren’t convinced that displacing so much of the cognitive work of medicine onto AI is the right way to remedy the doctor shortage. There’s a big gap in expertise between doctors and AI-enhanced medical assistants, says Emma Pierson, a computer scientist at UC Berkeley. Jumping such a gap may introduce risks. “I am broadly excited about the potential of AI to expand access to medical expertise,” she says. “It’s just not obvious to me that this particular way is the way to do it.” AI is already everywhere in medicine. Computer vision tools identify cancers during preventive scans, automated research systems allow doctors to quickly sort through the medical literature, and LLM-powered medical scribes can take appointment notes on a clinician’s behalf. But these systems are designed to support doctors as they go about their typical medical routines. What distinguishes ScopeAI, Goodner says, is its ability to independently complete the cognitive tasks that constitute a medical visit, from eliciting a patient’s medical history to coming up with a list of potential diagnoses to identifying the most likely diagnosis and proposing appropriate next steps. Under the hood, ScopeAI is a set of large language models, each of which can perform a specific step in the visit—from generating appropriate follow-up questions based on what a patient has said to to populating a list of likely conditions. For the most part, these LLMs are fine-tuned versions of Meta’s open-access Llama models, though Goodner says that the system also makes use of Anthropic’s Claude models.