ChatGPT, Claude, or the built-in Stockfish engine: pick your opponent and play in seconds. Then let the engine break down any game move by move: accuracy, blunders, and the turning points.
by Max Health Inc.
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Chess has long served as a benchmark for artificial intelligence. Dedicated engines like Stockfish and AlphaZero play at superhuman levels using search trees, evaluation functions, and reinforcement learning. They prove that machines can master complex decision-making when the boundaries are well defined.
Healthcare shares that aspiration: use AI to make better decisions, faster. Train systems to recognize patterns, weigh options, and recommend the action that leads to a better outcome.
So this project puts both kinds of AI on one board. You can play the bundled Stockfish engine, exact and calculating and reliable, or play a large language model like ChatGPT or Claude, which approximates reasoning by pattern-matching on its training data instead of calculating lines. We keep the model legal by only offering it valid moves and checking its choice server-side. Even so, it will miscalculate positions, miss tactics, and confidently propose losing plans.
Those are the very failure modes we see when LLMs are applied to healthcare: overconfident recommendations, missed context, and blind spots in the edge cases. The difference here is that the same engine grades every move, so each mistake is visible immediately rather than discovered too late.
That makes this a live playground for the question healthcare actually faces: where does general-purpose AI fall short, and how do we measure it against the truth? The engine is the ground truth; the language model is the system under test. It is exactly the kind of evaluation we need to build AI you can trust with far more than a chess game.