Eliminating Bias with Human-Centric AI

Traditional keyword searching involves entering specific words or phrases into a search engine, typically returning hundreds or thousands of papers with these keywords in their titles or abstracts.¹ However, keyword ambiguity and lack of context can yield irrelevant results,² making it difficult the separate the signal from the noise. Faced with this, most scientists resort to reading just the title and abstracts, introducing selection bias into the papers they choose to read. Additionally, keyword searching can miss crucial information, leading to skewed representation of  research landscapes.

Causaly’s AI solution reads the entire volume of biomedical literature and data in seconds, finding answers to complex research questions without bias. By machine-reading the literature, Causaly separates the signal from the noise, extracting only the most relevant insights from comprehensive data sources. Importantly, Causaly is fully transparent, providing users with all source information, enabling users to apply their own judgement to supporting evidence.  As an example, we used Causaly Cloud to identify and prioritize targets for gout, a form of arthritis which causes severe joint pain.

Using Causaly, almost 700 targets for gout were identified, extracted from 1,600+ supporting documents. All targets can be visualized as a dendrogram view of results, showing the type of target-disease relationship, and additional information including GWAS or clinical data.

Using Causaly’s advanced filtering capabilities, targets can be prioritized by desired criteria including by GWAS studies, clinical trials, primary data, animal models and novelty (Figure 2).

Searching “Targets for gout” in PubMed returned more than 1,500 results. Instead, Causaly’s human-centric AI uncovers the entire target landscape (~700 targets from 1,600+ documents), extracting relevant scientific insights, rather than just papers, from multiple data sources. By eliminating bias, researchers can make better-informed decisions, thereby enhancing the success rate of new drug development programs.

Want to see how Causaly can accelerate your research?