Chinese medical machine reading comprehension question-answering (cMed-MRCQA) is a critical component of the intelligence question-answering task, focusing on the Chinese medical domain question-answering task. Its purpose enable machines to analyze and understand the given text and question and then extract the accurate answer. To enhance cMed-MRCQA performance, it is essential to possess a profound comprehension and analysis of the context, deduce concealed information from the textual content and, subsequently, precisely determine the answer's span. The answer span has predominantly been defined by language items, with sentences employed in most instances. However, it is worth noting that sentences may not be properly split to varying degrees in various languages, making it challenging for the model to predict the answer zone. To alleviate this issue, this paper presents a novel architecture called HCT based on a H ierarchically C ollaborative T ransformer. Specifically, we presented a hierarchical collaborative method to locate the boundaries of sentence and answer spans separately. First, we designed a hierarchical encoding module to obtain the local semantic features of the corpus; second, we proposed a sentence-level self-attention module and a fused interaction-attention module to get the global information about the text. Finally, the model is trained by combining loss functions. Extensive experiments were conducted on the public dataset CMedMRC and the reconstruction dataset eMedicine to validate the effectiveness of the proposed method. Experimental results showed that the proposed method performed better than the state-of-the-art methods. Using the F1 metric, our model scored 90.4% on the CMedMRC and 73.2% on eMedicine.

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.

Background and Objective: Medical visual question answering requires reconciling clinical im-age content, structured biomedical knowledge, and natural language, yet prevailing architectures fuse these modalities once, in a single pass, and delegate all remaining cross-modal reasoning to a large language model. We propose an intelligent question-answering system that treats grounding and generation as distinct problems and performs explicit iterative grounding before generation begins. Methods: A Reasoning State Space Module (RSSM) maintains a reasoning state refined over 𝐾 cross-attention steps spanning image features, UMLS knowledge-graph embeddings, and intra-class visual prototypes, mirroring the cyclic hypothesis-revision of clinical reasoning. An Evidence Transport Module (ETM) then distils the final state into 𝐿 compact evidence tokens inserted immediately after the beginning-of-sequence token, placing the grounded evidence within the causal span of every generated token. The two modules are instantiated on the pre-aligned tri-modal embedding space of the BaMCo framework with a 3B-parameter LoRA-adapted backbone and trained end-to-end on a single GPU with a composite objective. Evaluation uses exact-match accuracy on the Slake and VQA-RAD benchmarks over three seeds, with paired bootstrap significance testing. Results: The system reaches 79.8% average accuracy on VQA-RAD and 87.0% on Slake. On VQA-RAD it exceeds a 14.7B-parameter generalist biomedical foundation model by 8.7 points, with gains concentrated in open-ended questions (+5.7 points over the base framework), while using roughly 4.9× fewer parameters. Both improvements over the base framework are statistically significant (𝑝 < 0.05 on Slake, 𝑝 < 0.01 on VQA-RAD). Ablations show the two modules are super-additive, each removing the constraint that limits the other, and characterise the effects of reasoning depth and evidence capacity. Conclusions: Explicit iterative grounding in representation space, rather than generative scale, is the decisive factor for compositional medical questions, yielding an accurate and parameter-efficient clinicalquestion-answering system.