A lab scientist viewing samples with a microscope.
The field of life sciences is expansive, accounting for billions of dollars in research, development and innovation spend annually across the globe. Over the past few decades, this flourishing field has provided humanity with some of the most cutting edge developments— from new advancements in pharmaceuticals and biotechnology, to medical devices and various means to deliver more personalized patient care.
Generative AI (Gen AI) has played an incredibly crucial role in this field. Innovators have developed a strong appetite for the potential with this technology— specifically with regards to transforming workloads, making processes more efficient and even aiding in the drug discovery and development process.
Shweta Maniar, Global Director of Healthcare and Life Sciences at Google Cloud, is optimistic about Gen AI and explains that there are numerous opportunities for the technology to positively impact life sciences, including in the fields of drug discovery, patient personalization and the regulatory arena. In fact, Maniar predicts that 2025 will be formative year for the intersection of Gen AI and life sciences, primarily in the following four ways:
- AI will increasingly become multimodal and provide novel solutions for data limitations. Models will increasingly be able to query and digest data from a variety of different sources. This advancement quells an incredibly challenging problem; data is frequently dispersed across numerous formats, and research and development often occurs within the confines of limited available data. Maniar describes how Bayer, one of the world’s largest pharmaceutical companies, is leveraging Gen AI to overcome the very specific challenge of working with limited data sets; the company is utilizing synthetic images generated from histology data to augment the limitations of existing training data in order to derive insights.
- There will be growth in the use of AI agents, specifically to improve workflows, increase efficiency and provide more value to enterprises and end-users. Examples of this are already evident in the industry, ranging from customer facing chat bots to many companies (even outside of life-sciences and healthcare) leveraging AI agents internally to help employees on a day-to-day basis.
- Search capabilities will continue to rapidly advance, especially with the introduction of more intuitive and natural ways to do so. In life sciences, traditionally tedious tasks such as combing through regulatory documents, performing literature reviews and developing clinical trials processes has incredible potential to be transformed with this technology. For example, AI powered search can quickly digest large swaths of information and generate cohesive responses that can quickly augment workflows. Especially with the introduction of natural language processing (NLP), humans can interact with these systems in significantly easier ways.
- Gen AI will transform patient engagement and the customer experience. Patient communication is one of the most important aspects of life sciences work. Whether with regard to counseling a patient on the complicated impacts of a medication, to delving into the nuances of a clinical trial with enrolled individuals, Gen AI can help simplify communication and notably make the experience more pleasant. Furthermore, the ability for Gen AI to work across different languages and cultural contexts can help life science companies further harness the technology to cater to the needs of diverse patient groups.
Maniar approaches all of this innovation with caution, however. She talks about how, despite all of this incredible momentum and investment in improving Gen AI, the focus for 2025 and for the future in general should also be to establish trust by developing the technology in a sophisticated and ethical manner: “realizing [the] full potential [of Gen AI] will require the industry to continue its strong focus on ethical considerations, data privacy, and cross-industry collaboration.” Without this focus on trust, she explains, the field can only progress so far.
Google Cloud is undoubtedly one of the pioneers in generative AI work. Especially with regards to life sciences, the company has made significant strides in the industry. In a broader sense, the field of generative AI has grown remarkably in recent years, and the competition among tech titans is fierce. The latest in this arena includes Amazon’s news last week that it will be investing an additional $4 billion into Anthropic, which is widely known to be the top competitor to OpenAI. Amazon itself has become a leader in generative AI with its Bedrock platform, and has also made numerous strides in the healthcare and life sciences spaces. Congruently, organizations are also increasingly willing to make bets on the technology. I recently wrote about Tenet Health and its landmark partnership with Commure to deploy the latter’s ambient AI platform across Tenet’s organization. Though not specific to life sciences, the deal highlights the general trend of enterprises attempting to leverage AI in order to improve workflows.
Ultimately, while the above four concepts have been labeled as predictions for the coming year, the reality is that the work behind each of these is already unfolding at a meteoric pace. As enterprises grow, workflows become more complicated and workforce challenges continue to amass, technology will ultimately be invoked to provide solutions. As the field of Gen AI continues to mature, the value and return on investment behind these innovations will undoubtedly and increasingly becoming self-evident.
