Research on human-in-the-loop (HITL) machine learning focuses on integrating human expertise into the machine learning pipeline to improve model performance, interpretability, and decision-making. Key topics include interactive labeling and annotation, where humans provide feedback on data quality or class labels to enhance training; active learning strategies that select the most informative samples for human review; and human-guided model debugging and feature engineering to correct biases or errors. Studies also explore collaborative frameworks where human insights complement automated algorithms in domains such as healthcare, autonomous systems, and natural language processing. Additionally, research addresses the balance between human effort and algorithmic efficiency, as well as methods to quantify and incorporate human uncertainty. Overall, HITL approaches aim to create more accurate, transparent, and robust machine learning systems by leveraging the complementary strengths of humans and machines.