The contemporary clinical management of stroke has entered an era in which predictive intelligence, personalization, and large-scale data integration are no longer aspirational ideals but methodological imperatives. The rapid proliferation of artificial intelligence techniques has transformed how clinicians and researchers conceptualize prognosis, rehabilitation potential, and treatment responsiveness in cerebrovascular disease, particularly when the heterogeneity of stroke pathology, patient physiology, and neuroplastic recovery trajectories are taken seriously. The foundational argument that stroke recovery is fundamentally individualized, rather than population-averaged, has been rigorously articulated within neurological science through advances in computational modeling, multimodal neuroimaging, and machine-learning-driven outcome prediction frameworks (Bonkhoff and Grefkes, 2022). At the same time, the healthcare data landscape has become structurally fragmented across hospitals, imaging centers, wearable devices, and electronic health record systems, rendering centralized artificial intelligence both ethically and practically constrained. Federated learning, personalized optimization, and distributed model architectures have therefore emerged as pivotal mechanisms through which individualized clinical intelligence may be reconciled with privacy, data governance, and cross-institutional collaboration (Kairouz et al., 2019). This article develops a unified theoretical and methodological framework for precision stroke outcome prediction through the synthesis of personalized federated learning, meta-learning, and optimization theory. Rather than treating stroke prediction as a static supervised learning problem, this work conceptualizes it as a dynamic, patient-specific adaptation process governed by neurological heterogeneity, institutional data biases, and evolving clinical context. Drawing on the insights of mixture-of-experts modeling (Chen et al., 2022; Feffer et al., 2018), hypernetworks (Ha et al., 2017), personalization layers (Arivazhagan et al., 2019), and adaptive federated optimization (Deng et al., 2020), we argue that stroke prognosis must be modeled as a continuously personalized inference process rather than a single global predictive mapping. The results demonstrate that personalized federated architectures provide a superior conceptual foundation for stroke outcome prediction than either centralized or purely global federated models. By interpreting performance through theoretical convergence, representational alignment, and adaptation capacity rather than numerical benchmarks, this study shows how distributed personalization can reconcile data heterogeneity, privacy, and clinical interpretability in a single coherent framework. The discussion situates these findings within broader debates in precision medicine, optimization theory, and machine learning, arguing that the future of neurological artificial intelligence lies not in larger models, but in models that are better aligned with the individuality of human biology and experience.

precision medicine, stroke prognosis, federated learning

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