Towards Trustworthy AI-Assisted Healthcare: Review on Studies Integrating LLMs, Multimodal Analysis, and Collaborative Filtering for Personalized Diagnosis

The accelerated growth of digital health records, multimodal patient data, and unstructured clinical narratives has overburdened the conventional recommendation systems used in healthcare, and they are incapable of working with complex long-term histories, contextual logic, and multimodal integration. Although Large Language Models (LLMs) have improved natural language understanding and decision support, there are common issues that prevent them, such as hallucinations, insufficient interpretability, safety risks, domain bias, inconsistent reactions, and unreliability across clinical domains, which inhibit the clinical reliability of Large Language Models. This study introduces a hybrid architecture, which is a synergetic integration of LLMs (contextual and reasoning), multimodal modules (clinical image and report analysis), and graph-based collaborative filtering to learn patient longitudinal interactions and collaborative cues. In order to solve hallucinations and uncertainty, the framework involves retrieval-augmented generation, multi-LLM ensemble uncertainty quantification, and knowledge-grounded verification. Tracing paths of reasoning, uncertainty maps, and justifications provided to clinicians to explain explanatory models is built into explainable models to build trust and validation. The system is strictly tested against actual clinical data and known standards (MedQA, MultiMedQA, MEDHALU, and other emerging suites of practice applications such as HealthBench and DiagnosisArena). Early findings show that it is more accurate in diagnostic procedures, has fewer hallucinations (lowering it to less than 2 percent), achieves greater safety in adversarial use, and personalizes better than either standalone LLM or conventional methods. The paper paves the way for creating safe, equitable, and clinically viable decision support tools by filling the knowledge-based reasoning-collaborative longitudinal recommendation gap, which can empower human expertise and not replace it.