Object detection models like YOLO excel at real-time, frame-level inference and producing clean bounding box outputs, but they fall short when it comes to understanding video as data. The limitations arise in system design rather than model performance, as frame-level predictions do not naturally support temporal reasoning, nor do they provide a searchable or queryable representation. Additionally, audio, context, and higher-level semantics are often disconnected, highlighting the difference between identifying objects in a frame and understanding the events in a video. The focus needs to shift towards building pipelines that incorporate temporal aggregation, multimodal fusion, and systems that enhance rather than replace models. This approach aims to address the complexities of video analysis, emphasizing the need for both advanced models and robust systems. Understanding these limitations is crucial for developing comprehensive video intelligence solutions.
![[P] Physician → NLP in 48 hours: Building a clinical signal extraction pipeline during my December break](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-7517-300x171.png)
A psychiatrist with an engineering background embarked on a journey to learn natural language processing (NLP) and develop a clinical signal extraction tool for C-SSRS/PHQ-9 assessments within 48 hours. Despite initial struggles with understanding machine learning concepts and tools, the physician successfully created a working prototype using rule-based methods and OpenAI API integration. The project highlighted the challenges of applying AI in healthcare, particularly due to the subjective and context-dependent nature of clinical tools like PHQ-9 and C-SSRS. This experience underscores the need for a bridge between clinical expertise and technical development to enhance healthcare AI applications. Understanding and addressing these challenges is crucial for advancing AI's role in healthcare.