Artificial intelligence / machine-learning tool for post-market surveillance of in vitro diagnostic assays.

The study compares an artificial intelligence technology with traditional manual search of literature databases to assess the accuracy and efficiency of retrieving relevant articles for post-market surveillance of in vitro diagnostic and medical devices under the Medical Device Regulation and In Vitro Diagnostic Medical Device Regulation. Over a 3-year period, literature searches and technical assessment searches were performed manually or using the Huma.AI platform to retrieve relevant articles related to the safety and performance of selected in vitro diagnostic and medical devices. The manual search involved refined keyword searches, screening of titles/abstracts / full text, and extraction of relevant information. The Huma.AI search utilized advanced caching techniques and a natural language processing system to identify relevant reports. Searches were conducted on PubMed and PubMed Central. The number of identified relevant reports, precision rates, and time requirements for each approach were analyzed. The Huma.AI system outperformed the manual search in terms of the number of identified relevant articles in almost all cases. The average precision rates per year were significantly higher and more consistent with the Huma.AI search compared with the manual search. The Huma.AI system also took significantly less time to perform the searches and analyze the outputs than the manual search. The study demonstrated that the Huma.AI platform was more effective and efficient in identifying relevant articles compared with the manual approach.

Customer Centricity in Medical Affairs Needs Human-centric Artificial Intelligence.

The evolution of healthcare, together with the changing behaviour of healthcare professionals, means that medical affairs functions of pharmaceutical organisations are constantly reinventing themselves. The emergence of digital ways of working, expedited by the COVID-19 pandemic, means that pharmaceutical-healthcare relationships are evolving to operate in an increasingly virtual world. The value of the pharmaceutical medical affairs function is dependent on understanding customers' needs and providing the right knowledge at the right time to physicians. This requires a human-centric artificial intelligence (AI) approach for medical affairs, which allows the function to query internal and external data sets in a conversational format and receive timely, accurate and concise intelligence on their customers.