Medical Visual Question Answering (Med-VQA) integrates medical image understanding and natural-language reasoning, but strong benchmark results do not yet establish clinical reliability. This structured review focuses on representative Med-VQA models published from January 2022 through 30 September 2026, while retaining earlier methods and datasets only as historical and technical context. The revised review reports its information sources, search strategy, eligibility criteria, screening logic, and data-charting procedure. It compares classification-based and generation-based systems according to their answer-production mechanism, surveys ten public datasets, and critically interprets reported performance in light of heterogeneous data splits, preprocessing, answer vocabularies, and evaluation protocols. Particular attention is given to visual grounding, hallucination, domain generalization, experimental randomness, reproducibility, and the gap between benchmark accuracy and clinical validation. The review concludes that progress toward trustworthy Med-VQA requires evidence-grounded answers, uncertainty-aware evaluation, repeated-seed reporting, external validation, and sustained human oversight.