From Asimov's laws to Kasparov's laws: artificial intelligence, clinical work, and the design of hybrid intelligence

The article explores the shift from Asimov's laws, centered on machine obedience, to Kasparov's laws of "hybrid intelligence". While Asimov focused on preventing harm through autonomous constraints, Kasparov emphasizes that the best performance arises from the optimal orchestration of human, machine, and process. This perspective suggests that a "weak human + machine + superior process" can outperform a "strong human + machine + inferior process". Empirical studies in radiology are consistent with this socio-technical conjecture. Studies in radiology indicate that specific collaboration protocols allow human-AI teams to surpass isolated models. Notably, research confirms that less proficient clinicians embedded in effective protocols can achieve higher accuracy than clinicians with higher baseline accuracy operating under less effective protocols. This framework views AI as a component of "superminds" - collective cognitive architectures that enhance plural decision-making. Ultimately, the value of AI is an emergent property of the organizational system. Rather than focusing solely on model accuracy, designers must create interaction protocols that calibrate trust and prevent professional deskilling. The goal is to move toward a synergy where machines help human collectives become more intelligent.