Key opinion leaders discuss how to approach challenges and opportunities which arise when looking to optimise lab processes.

As the capacities of multi-omics integration continue to grow, key opinion leaders discuss the main setbacks and outlooks for the field.

How will SmartLab development be shaped in the coming years as AI/ML integration continues to be implemented?

How are AI integration and the growing capabilities of machine learning impacting drug discovery and clinical trials?

The implementation of a streamlined and connected data ecosystem is critical to the transformation of healthcare technology and circumventing data regret.

Key opinion leaders from AbbVie, Genentech, and the University of Southampton ask, “how and why must we change the way we work with data?”

Key opinion leaders from GSK and the University of Southampton discuss the steps needed to reach the lab of the future.

From an industry perspective, refining the sharing and storing of healthcare data is essential for improving the efficiency of new research and development. Appropriate implementation of FAIR data strategies is a key step in achieving this.

With every new technological innovation, the pharmaceutical industry moves closer to the broader introduction of automation in production lines. But what bumps in the road to progress need to be smoothed out when it comes to the standardisation of industry approaches?

With the automation of cleanrooms set to continue for the foreseeable future, considering best practice approaches for integrating mobile robotics in a pharma-compliant manner will be key for addressing the needs of the industry. This Insight article explores some of the potential obstacles to the adoption of mobile robotics and considers how they could be addressed.

RWD now represents a major source of information for understanding current treatment results and improving future analysis. Its proper implementation in healthcare analysis has the potential to improve patient outcomes.

By taking a more holistic approach to the design of mobile robots, cleanroom automation and aseptic production in the manufacture of pharmaceutical therapeutics could be made into a reality.

Some of the biggest current focuses of machine learning and AI research and development include testing clinical scenarios to identify the patients who will benefit most from treatment. One of the main goals for researchers is increasing the transparency and understanding underpinning data sharing and AI technology.

Recent advances in AI and machine learning have huge potential to improve the accuracy and speed of drug design. This discussion group report explores some of the current applications of different AI/ML approaches, including deep learning techniques.

The focus in the pharmaceutical industry has been, and will remain, on generating cheaper and more detailed data through the applications of AI/ML techniques which can be interpreted more easily.

Data quality, bias, and standardisation are all important to account for when planning for federated and non-federated learning factoring in recurrent neural networks. Biobank frameworks offer the opportunity to augment healthcare data through the appropriate implementation of machine learning.

If some gaps in patient data are inevitable, then the use of RWE and RWD presents an opportunity to fill them in synthetically. Coordinated data sharing is an important aspect of consistent RWD usage.

In this Commentary article, we explore the applications of end-to-end automation in accelerating workflows through the use of robotics and informatics.

Robot healthcare assistants designed to operate in outer space may sound like science fiction, but the reality is incredibly feasible. In this Commentary article, Daniel Leidner explains how telerobotics-enabled hardware is currently being trialled in different laboratory environments as a means of providing remote healthcare.

In this Insight article, we explore SiLA: the consortium for Standardisation in Lab Automation, and look at some of the opportunities and challenges presented in the vision for a fully integrated laboratory environment.