Enterprise Multimodal Biomedical QA LLM

Prepare a reproducible, safety-aware engineering foundation for text biomedical question answering and medical image-text question answering without claiming a trained or clinically validated system.

PubMedQA supports biomedical text question answering. SLAKE supports medical image-text question answering and requires linked image-path validation.

Implemented work includes PubMedQA JSONL inspection with required-field validation and SLAKE inspection with image-path checks.

Text and image-text records share a common task_type, instruction, input, target, and metadata contract for consistent downstream processing.

Dataset provenance, licensing notes, configuration placeholders, explicit schemas, and deterministic future preprocessing plans keep the research path inspectable and repeatable.

Hermetic unit tests use synthetic fixtures so validation behaviour can be tested without network access or reliance on full external datasets.

Qwen2.5-Omni loading and prompt-only text and image-text baselines are planned. No model training or completed model integration is claimed.

Future stages include deterministic preprocessing, an evaluation framework, supervised fine-tuning, LoRA and QLoRA experiments, and multimodal evaluation. None are presented as completed.

This work is limited to research and educational engineering. It does not provide medical advice and is not intended to replace qualified healthcare professionals or support autonomous clinical decisions.

No trained model, completed QLoRA run, evaluation metrics, clinical validation, production deployment, or patient-specific use is claimed.

Potential later work includes model baselines, controlled fine-tuning experiments, evaluation, API exploration, Docker, CI/CD, monitoring, and deployment-oriented research.