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  A patient decides they need a specialist. A friend or their primary care doctor gives them a name. Not long ago, that name led straight to a phone call. Today, it leads to a search box—and increasingly, to an AI that decides whether the recommendation holds up. John Spencer Ellis, founder of Reputation Return, has built his work around this new checkpoint, helping trusted physicians make sure they're the ones who clear it rather than the ones quietly filtered out. The Search Habit Doctors Haven't Accounted For At the center of Ellis' message is a change in how patients look for care. Over half of patients under 50 now use AI platforms such as ChatGPT, Claude, Google AI Overview, Gemini, and Perplexity to find a physician. These tools behave nothing like the search results doctors are accustomed to. Instead of returning a list to browse, they surface only three to five named providers per query, and everyone else effectively disappears. Ellis makes the stakes concrete. ...

 Millions of customers ask AI platforms for business recommendations every day. Some businesses consistently receive favorable mentions. Others remain invisible regardless of quality. The difference isn't random—AI follows patterns when deciding who to recommend. Understanding the specific signals AI evaluates when forming recommendations reveals exactly what determines whether customers hear your name or a competitor's when they ask AI for guidance. AI Recommendation Isn't Magic—It's Signal Evaluation AI platforms don't randomly select businesses when users ask for recommendations. They evaluate available signals, weigh evidence, and form opinions they express as confident suggestions. This evaluation process follows identifiable patterns. Businesses demonstrating certain characteristics receive favorable AI treatment. Those lacking these characteristics get overlooked—even when actual quality exceeds competitors AI does recommend. Learning which signals AI pr...