In the interview, Robert Parry talks about the unique uses of their platform in public health research. He says: “The intention is to bring visibility to the work being done in these areas so that people interested in connecting with the experts who are gaining traction in scientific circles can find them and quickly understand how important these people are. are influential in certain areas.
In medicine, doctors who do research are called opinion leaders. Research needs to be unbiased, so advisors are often brought in to help set people’s expectations and communicate with them about emerging treatments. Reputable scientists can be critical to a company’s ability to communicate its message. Key Opinion Leaders, the company, helps hospitals in Africa connect with doctors who are experts in HIV/AIDS research.
You are a hospital facing a shortage of doctors and you want to hire the best serologists in the country. How can you identify them? One way is to use “unsupervised machine learning (ML) for reputation modeling and rank 50 million biomedical researchers based on their reputation,” according to a KOL press release. The AI-based engine allows universities, hospitals and labs to examine researchers’ “perceived trustworthiness” for specific concepts. This reliability score is not the number of calls or citations but a quantifiable level of confidence for each researcher. “Contextual reliability” is the centerpiece of this technology, Parry added.
In other words, this product integrates with your existing recruitment tools and delivers an automated score for each candidate. Then it will match candidates based on how they rate technical and contextual attributes.
The example below shows how contextual reliability works for a research-focused hospital: the AI engine assesses each candidate’s area of interest and contextual reputation based on their connections and activity within that spectrum. of subjects. This Reliability Score allows recruiters to compare candidates side-by-side based on who has the most topical expertise and reputational potential from topical comparisons and compare each candidate’s connectivity and activity across all the subjects in which they are active in the field of health work.
“Since we started implementing contextual trust AI to help researchers identify influential peers (whether within academia or industry), we have seen dramatic improvements in two parts; 1) A better distribution of knowledge between researchers 2) A reduction in knowledge silos. As these silos are closed by contextual reliability AI, researchers can see which concepts are most important, who is impacting which areas of research, and how they are creating that impact. Mr. Parry added.