Topic: Life sciences and healthcare

As recent innovations in Artificial Intelligence (“AI”) such as ChatGPT ignite debate over issues such as the legal status of non-humans, privacy implications of novel computing abilities, and rights of intelligent systems’ creators over output, similar considerations confront another emerging computing field—one involving human cells.

The European Commission (along with the US FTC and DOJ, UK CMA, German FCO and others), has been focused on ensuring that so-called “killer acquisitions” in the digital and life sciences sectors are reviewed. Against this background, the General Court’s decision in Illumina/Grail is of key importance to companies acquiring start-ups.

As the telemedicine industry continues to grow, especially in light of COVID-19, businesses should reconsider their policies and procedures in connection with telehealth services and user safety.

In Part 3, we highlight some of the legal issues surrounding the use of AI/ML in collaborative drug discovery and some solutions for addressing these issues.

Part 2 looks at an historic cross-competitor collaboration in the EU that is also leveraging blockchain technology to share large amounts of data to improve predictive models in drug discovery.

In Part 1 of this update for 2019, we outline some of the key areas of advancements and discuss some of the recent partnerships between pharma companies and AI companies.

The Guidelines are the first deliverable under the European Union (EU’s) April 2018 Communication on Artificial Intelligence for Europe.

The European Patent Office (EPO) recently issued guidance on the patenting of artificial intelligence (AI) and machine learning (ML) technologies. What does this mean for businesses in the field?

Does the Database Directive protect machine-generated data and AI?  That is one of the questions the European Commission considered in its recently published second evaluation of the Database Directive.

AI’s impact on the workplace is contingent on many variables - the level of employees’ education and training, the cost of the technology, its adoption rate, regulation, ethics and how far AI creates new jobs.