The integration of artificial intelligence (AI) into medical informatics presents significant opportunities to enhance healthcare through data-driven diagnostics, predictive analytics, and personalized therapeutic recommendations. This paper examines the role of general intelligence in improving the effectiveness and adaptability of AI systems in complex clinical environments. We explore various levels of generalization – local, broad, and extreme – highlighting their respective contributions and limitations in healthcare. Local generalization provides robust assessments based on well-defined risk factors, while broad generalization allows for nuanced patient stratification across diverse populations. Extreme generalization, however, presents the greatest challenge, requiring AI systems to adapt to entirely new contexts without prior exposure. Despite advancements, existing metrics for assessing generalization difficulty remain inadequate, necessitating the development of new evaluation methodologies.