Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of AI-based antibody generation and the often overlooked non-animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high-quality antigen formulations. This journey has traversed various stages, from methods involving serum-based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in-depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum-based acquisition, lengthy and difficult hybridoma-derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.