Metadata is a crucial aspect of implementing FAIR data principles and practices. Without metadata, you have no view of what is going on with your unstructured data, context as to what the data is or what it is for.
There are many reasons why it is crucial to understand how an AI-enabled system has led to a specific output. Explainability can help developers ensure that the system is working as expected, meet regulatory requirements, or reveal the importance and effects of data previously deemed unimportant by humans.
In this Commentary article, we explore some of the applications of synthetic data generation in the context of healthcare and treatment. Synthetic data can overcome problems surrounding privacy and enable the sharing of important medical information without compromising the privacy of patients.
This Commentary article revisits the development and implementation of mobile health data, exploring how initiatives such as the NHS track and trace system helped to mitigate the worst of the impact of the Covid-19 pandemic. Here, we consider the applications of mHealth data for present and future pandemic situations.
Digital biomarkers are rapidly being adopted to provide quality insights into patient health. David Nobbs, Biomarker Disease Area Lead at Roche and Valentin, Data Analytics Leader at GSK share how their companies are using digital biomarkers.
This Commentary piece looks at the means by which the global approval process for new medicines can be sped up through the controlled use of FAIR data. Key areas of focus include the sharing of healthcare data to bring the patient voice within the clinical setting.
Key opinion leaders discuss the latest trends and challenges in laboratory automation and digitalization. Key insights include an explanation of Roche's 'Levers of Production' method, the challenges of legacy systems and the possibility of digitalization overload.
Digital tools to boost productivity and optimise pharma manufacturing are becoming increasingly prevalent through smart sensors, AI, ML, virtual reality, and cloud computing. Digital transformation is here for Pharma, and it is changing companies' priorities towards digital solutions.
The amount of data created by pharmaceutical companies has increased exponentially over the last few decades. Managing these enormous volumes of data can be challenging, an issue compounded by a lack of standardisation. Data is often gathered from numerous sources and devices and stored separately, making it difficult to retrieve, query, share and analyse.
This September, our PharmaTec series discussion group focused on cutting edge data analytics. Some of the key topics our experts covered included whether analytics should start with the data or a use case first, different analytical platforms and the application of AI/ML technologies.
We produce cutting edge congresses and summits for the Life Sciences Industry, bringing together industry leaders and solution providers at a senior level, creating the opportunity to partner, network and knowledge share.