Artificial intelligence (AI) is becoming increasingly important in our personal and professional lives. However, there appears to be some confusion regarding terms such as “artificial intelligence”, “machine learning (ML)”, “data science (DS)”, “office automation (OA)”, and “robotic process automation” (RPA), which should not be used interchangeably.
The recent availability of health-related Big Data – i.e. of data sets that are too massive or complicated for typical data-processing systems – entails also a technical challenge and a big opportunity for pharma companies.
Advanced digital technologies have already found a home in the early stages of a drug’s lifecycle (e.g., Drug Discovery), but less so in the later stages.
Drug development and drug registration are fascinating, as they provide novel technological challenges. For example, the “signal to noise ratio” and “quality” of clinical data vary greatly between clinical trial reports and information from social networks, smartphone applications, or wearables.
Also, many activities need a combination of administrative and repetitive tasks (such as data cleaning and form filling) and of other tasks that necessitate a high degree of skill and competence.
Technology today provides a wide range of solutions, ranging from “basic” procedure automation (which allows nevertheless for considerable benefits in terms of efficiency and quality) to very advanced Natural Language Processing (NLP) solutions (which show some very interesting results, but which are not yet fully operational).
A sensible strategy from a Pharma executive could be to begin deploying existing available solutions in his/her department and reaping the advantages while monitoring the landscape for new advancements.
Recent developments in the area have revealed that the landscape is changing rapidly.
Social networks, for example, changed from “indispensable source of knowledge” to “secondary and optional” in months.
In addition, Drug Development has specific special criteria, and the typical measures used to assess the suitability of a machine learn (ML) based system, such as precision and accuracy, may not be enough.
The “human aspect” should not be disregarded either.
The introduction of automated technologies raises in fact some critical ethical, managerial, and legal issues that stakeholders and authorities are only now beginning to address. These considerations include the final liability in the event of a crisis, the existence of erroneous (or purposefully misleading) information, and the influence of these new technologies on the workforce.
A significant number of development operations can benefit from currently existing and operational technology, resulting in significant gains in efficiency and quality. The most rational strategy, which has already been examined or adopted by some companies, is for a re-analysis of corporate processes and the progressive “modularization” of those that may benefit the most, namely those that are time-consuming, repetitive, and error-prone.
The modules might then be “sewn together” to achieve the goal of a completely integrated and automated system, allowing specialists to focus on the most essential and relevant aspects of their profession.
Would you like to know more about this topic?
JOIN OUR FREE WEBINAR
ArtificiaI Intelligence: Not Only for Drug Discovery
How Data Science, Artificial Intelligence, Machine Learning and Automation can help Pharma companies in Phase III and Phase IV activities
The scope of this webinar is to give participants a high-level view of the subject and to help them make informed decisions and plan/implement strategies on the use of AI and other advanced technologies in the late development and post-marketing phases of the life cycle of a drug
Trainer: Marco Anelli, Medical Affairs and Pharmacovigilance consultant
Marco has a medical degree from the University of Milan, specializations in Medical Statistics and Clinical Pharmacology from the University of Pavia and an international master’s degree in health economics and pharmacoeconomics from the University of Pompeu Fabra in Barcelona, plus formal training in Data Science and Artificial Intelligence. In the last few years, he has extensively worked in the fields of pharmacoeconomics and health technology assessment. Marco Anelli has been a free-lance consultant in Medical Affairs and Pharmacovigilance/ Drug Safety since 2022.
Duration: 90 minutes
Date: 06 March 2023 from 4.00 pm to 5.30 pm