Markus Strasser details his experiences with biomedical literature search and discovery applications, revealing a disillusioning outcome where the industry’s efforts in semantic structuring and synthesizing academic papers hold little real-world value. Despite the initial promise, the commercial viability of these technologies is questioned, with large players failing to capitalize on them and smaller ventures struggling or pivoting to stay afloat.
- The commercial industry focused on structuring academic literature for easier discovery through advanced NLP applications struggles to prove its value in the practical field, with many initiatives failing to achieve significant impact.
- Even well-funded and technically proficient organizations like AllenAI face challenges in commercializing their semantic search and knowledge discovery tools, indicating a lack of market demand for these applications.
- Strasser’s own attempts to build and sell biomedical NLP applications to biotechs left him skeptical about the sector’s potential, further evidenced by the modest financial success of NLP companies compared to bioinformatics drug discovery platforms.
- Despite high expectations, the tools designed to enhance research output discoverability and semantic search capabilities have not become mainstream nor indispensable in the scientific community.
- Strasser concludes that while bioinformatic knowledge apps that integrate various data types are valuable, those predominantly leveraging published academic literature are not solving a significant problem in biomedical literature search and discovery.