The average drug development timeline, from discovery to the commercial launch of a new drug, is between 12 and 15 years. To overcome long timelines, high demand, clinical trial failures, and complex biomanufacturing processes, the industry is adopting AI. Advances in gen AI, neural networks, and LLMs enable improvement in predictive AI models. These predictive models can save significant time and costs in drug development while meeting the expected accuracy and quality levels in clinical outcomes. Biopharma companies are adopting AI-driven platforms to accelerate drug-target discovery, lead identification, lead optimization, and clinical trials across modalities. AI is also transforming advanced therapeutics, clinical decisions, regulatory assistance, and drug delivery system design and formulation. This report analyzes AI’s role in biopharma and explores AI’s technology advances, impact, and future evolution in drug discovery and bioprocessing. Scope Geography: all Industry: healthcare, pharmaceutical, AI in drug discovery, and AI in bioprocessing Contents In this report, we examine: AI in the biopharma industry Key factors driving R&D and AI adoption Key technology developers and adopters and innovation landscape The future trajectory of AI in biopharma

As the healthcare industry prioritizes precise, efficient, and accessible care, automation is essential in addressing challenges such as inaccuracies, inefficiencies, workforce shortages, and limited access to care. Robotic technologies help bridge these gaps and enhance healthcare delivery. Healthcare robots are transforming the industry by improving medical care’s precision, efficiency, and accessibility. They come in various forms and serve multiple applications, from surgery to rehabilitation. Integrating these robots into healthcare aims to enhance patient outcomes, minimize human error, and optimize care delivery. Additionally, hospitals use these robots to assist with logistics, streamline clinical workflows, and improve operational efficiency. By reducing the burden on healthcare staff, they contribute to long-term cost savings through increased efficiency and fewer errors. This research focuses on an in-depth analysis of non-surgical robots, exploring their technology advances, impact, and future evolution in healthcare. Scope Industry: healthcare, pharmaceutical, medical devices, and diagnostics All geographies Contents Focus areas of the report include: Non-surgical robots’ technology landscape in healthcare Key factors driving R&D and adoption Key technology developers and adopters and the innovation landscape Non-surgical robots’ future trajectory in healthcare