Artificial superintelligence alignment in healthcare

The emergence of Artificial Superintelligence (ASI) in healthcare presents unprecedented opportunities for revolutionizing diagnostics, treatment planning, and population health management, but also introduces critical risks if these systems are not properly aligned with human values and clinical objectives. This review examines the theoretical foundations of ASI and the alignment problem in healthcare contexts, exploring how misaligned Artificial Intelligence (AI) systems could optimize for wrong objectives or pursue harmful strategies leading to patient harm and systemic failures. Current challenges in AI alignment are illustrated through real-world examples from radiology and clinical decision-making, where algorithms have demonstrated concerning biases, generalizability failures, and optimization for inappropriate proxy measures. The paper analyzes key alignment challenges including objective complexity and technical pitfalls, bias and fairness issues in healthcare data, ethical integration concerns involving compassion and patient autonomy, and system-level policy challenges around regulation and liability. Technical alignment strategies are discussed including reinforcement learning from human feedback, interpretability requirements, formal verification methods, and adversarial testing approaches. Normative alignment solutions encompass ethical frameworks, professional standards, patient engagement protocols, and multi-level governance structures spanning institutional, national, and international coordination. The review emphasizes that successful ASI alignment in healthcare requires combining cutting-edge AI research with fundamental medical ethics, noting that while proper alignment could enable transformative health improvements and medical breakthroughs, misalignment risks undermining the core purpose of medicine. The stakes of this alignment challenge are characterized as among the highest in both technology and ethics, with implications extending from individual patient safety to public trust and potentially existential risks.