A EUCROF (EUropean CRO Federation) article co-written by Marco Anelli, MD - Senior Medical Advisor at ProductLife Group, Milano
There seems to be a lack of clarity about the meaning of terms such as “artificial intelligence (AI)”, “machine learning (ML)”, “data science (DS)” and “office automation (OA)”, which should not be used interchangeably. Pharmacovigilance (PV) represents a very interesting field in this regard, since it poses some unique technical challenges. For example, the “signal to noise ratio” and the “quality of the information” are very different in reports from clinical trials and in information coming from social networks, smartphone apps or smartwatches, and therefore require different approaches. Case processing, moreover, requires a mix of administrative and repetitive tasks (e.g. data cleaning and form filling) and tasks that require a high level of experience and specialization (e.g. medical reviews and signal detection). Technology now offers a wide spectrum of solutions, which go from already available “simple” procedure automation allowing significant improvements in efficiency and quality to sophisticated Natural Language Processing (NLP) solutions, which have shown interesting results but are not yet fully operational. A sensible approach, from the perspective of a PV executive, could be to start implementing and reaping the benefits of already available solutions while at the same time keeping the landscape monitored for new developments. In the second part we will address some of the open issues, of organizational, social and regulatory relevance, concerning the implementation of AI- and ML-based technologies in our domain.
