Background: Traditional aphasia therapy is often limited by insufficient dosage, a barrier that AI-assisted digital therapies are poised to overcome. However, it remains unclear whether gains on specific tasks translate to functional, real-world communication. This systematic review evaluates the effectiveness of these novel interventions and investigates the potential for a “generalization gap” when compared to conventional treatments for post-stroke aphasia rehabilitation. Methods: Following PRISMA guidelines, we systematically reviewed randomized controlled trials (2010–2024) from six databases. We included studies examining AI-powered digital platforms for adults with chronic post-stroke apha-sia that reported standardized language outcomes. Results: Our analysis of five trials (n = 366) shows that AI-assisted therapies successfully deliver high-dose interventions, leading to significant improvements in trained language skills, including word retrieval (up to 16.4% gain) and auditory comprehension. However, a critical “generalization gap” was consistently identified: these impairment-level gains rarely transferred to functional, real-world communication. Conclusions: AI-assisted digital therapies effectively solve the dosage problem in aphasia care and improve specific linguistic deficits. Their primary limitation is the failure to generalize skills to everyday use. Future platforms must therefore be strategically redesigned to incorporate therapeutic principles that explicitly target the transfer of skills, bridging the gap between clinical improvement and functional communication.
Liscano et al. (2025) studied this question.