Assertion status detection is critical in clinical NLP but often overlooked, leading to underperformance in commercial solutions like AWS Medical Comprehend, Azure AI Text Analytics, and GPT-4o. We developed advanced assertion detection models, including fine-tuned LLMs, transformers, few-shot classifiers, deep learning (DL) and rule -based approaches. Our fine-tuned LLM achieves 0.962 accuracy, outperforming GPT-4o (0.901) and commercial APIs, with notable improvements in Present (+4.2%), Absent (+8.4%), and Hypothetical (+23.4%) assertions. Our DL models also excel in Conditional (+5.3%) and Associated with Someone Else (+10.1%) categories. The few-shot classifier (0.929 accuracy) provides a lightweight alternative for resource-limited environments. Integrated with Spark NLP, our models offer scalable, transparent, and domain-adapted solutions that surpass black-box commercial APIs in medical NLP tasks.