Analyzing 2023 Technology Trends in the Life Sciences Industry

October 25, 2023 - The healthcare industry has been thrust into the digital transformation era as new technologies invade nearly every healthcare sector. From streamlining workflows for healthcare providers to advancing the development of new drugs or therapies for pharmaceutical companies, technology has had implications in clinical development, new treatment discovery, healthcare automation, and other healthcare sectors.  

LifeSciencesIntelligence interviewed Tracy Ring, Global Head of Generative Artificial Intelligence (GenAI) for Life Sciences and Chief Data Officer at Accenture, to discuss 2023 digital technology trends in the life sciences industry and healthcare ecosystem. 

Rapid Innovation 

One of the primary trends researchers and analysts highlighted is the ever-changing pace of biopharma innovation. According to the report, the convergence between science and intelligent technology has significantly condensed the innovation pipeline, allowing faster and more comprehensive discovery and product development.  

Approximately 93% of biopharma executives attribute the rapid speed of biopharma innovation to advancements in science technology. The compound annual growth rate (CAGR) of AI-mediated drug discovery reached 8% in 2021, and the total investment in 2022 reached $2.5 billion.  

Over the past five years, biopharma companies have invested nearly $1 billion in upfront payments, with the potential value of these investments reaching approximately $45 billion.  

“Generative AI startups have grown by 840 million in the last five years,” Ring noted, “That type of trajectory is a leading indicator.” 

Beyond investments, the life sciences sector is seeing more pharmaceutical industry collaborations with AI companies. 

“That permeability seems to be at an all-time high,” she added. “Following this investment trajectory and seeing biopharma investing a billion dollars in upfront payments in the last five years, these are not prototypical investments.” 

The potential value of these investments is driven by the impacts of GenAI and other technological advancements that have reduced the time and cost of biopharmaceutical development. For example, GenAI has optimized antibody discovery and accelerated development speed in the wet lab environment.  

Technology has helped scientists use real-world data to enhance precision medicine and the delivery of human-ready molecules, such as antibodies. For example, zero-shot GenAI can accelerate the speed of antibody development and increase the probability of antibody success.  

According to the Accenture report, the speed of technology development has implications in three areas of the life sciences and pharma industry.  

The first area of implications is technology and data. The report notes that having big data across multiple resources can complicate AI and machine learning (ML) analytical strategies.  

To utilize these resources more efficiently, life sciences companies must develop effective data strategies that reflect their business plan, stakeholder interests, and the ever-changing regulatory environment.  

Additionally, there are implications for organization and culture. Biotech companies must procure talent to understand the intersection of science and technology, which may also require balancing two vastly different work cultures.  

Finally, company strategies have focused on AI-mediated drug development or discovery for specific therapeutic areas. However, companies must extrapolate these strategies and apply them to the broader portfolio to attract partnerships and sponsors. 

Ring notes that AI integration will promote better outcomes. Companies are considering how to bring better, more innovative drugs to market, avoid phase three clinical trial failures, minimize the cost of getting a drug to market, and more.  

GenAI  

Beyond innovation speed, the researchers emphasize that generalizing artificial intelligence (GenAI) has become a vital and significant factor in the success of biopharma companies. GenAI is a type of AI that uses foundation and large language models (LLMs).  

According to the report, GenAI, while relatively new, has significantly changed the biopharma and overall life sciences industry, considerably altering the value chain. 

Approximately 70% of biopharma executives expect GenAI to facilitate faster decision-making. Furthermore, 55% predict that it will accelerate innovation. Additionally, 63% expect better customer experiences, and 60% anticipate improved communication.  

With these significant implications on the life sciences and pharmaceutical industry, Accenture predicts that roughly 40% of life sciences work hours will be impacted by AI, with alterations to the role of talent. 

“That does not mean that four out of every ten work hours will be unproductive or repurposed. It's that workers will either have their work augmented or automated. So there are things that will make them more efficient,” explained Ring. 

She notes that AI has disrupted work at every level and function, creating broad transformations in people's work. 

Data as the Lifeblood 

Another significant trend is the importance of data and data analytics in the research and development (R&D) process. Accenture repeats the phrase “data is the lifeblood of innovation” to highlight data's critical role in improved innovation and translating research into patient outcomes. 

Ring echoes Accenture’s stance that data is the lifeblood and epicenter of AI. She emphasized that data sharing is among the most exciting concepts to help compress the drug launch process and promote improved life sciences strategies. 

Generating and sharing data is expected to significantly improve scientific collaboration and promote faster, more accurate decision-making.  

“This concept around data sharing is probably the most nuanced regarding the value and the risk. As a greater population, we stand to have better outcomes, faster cures, more successful investments, etc.,” said Ring. 

Nearly all (97%) of executives need new strategies to manage and share data. Researchers incorporating AI may consider factors like how to share data with a broader consortium. How might they share research in a way that allows those receiving it to see the most valuable data?  

“The genesis of federated learning is being able to take multiple different data points and data sets and massive amounts of data to tune and train on,” Ring added. “Other industries have been doing this. The financial markets use SWIFT, and data has been shared in other industries for quite some time. It's not a novel idea.” 

Beyond data sharing, data transparency continues to be a focus area for these companies. The report notes that 92% of biopharma executives emphasize the need for data transparency in the competitive biotech landscape, with over 40% highlighting improved trust as a benefit of data transparency.  

Digital Identity  

The organization notes that digital identity will catalyze the next era of digital innovation. According to 90% of biopharma executives, developing a digital identity is a strategic business goal, allowing companies to improve data sharing and apply AI more effectively.  

Although digital identity began with enabling controlled access, new advancements in digital identity need to consider identifying individuals and objects, as these components will have implications in clinical trial onboarding, personalized medicine monitoring, and patient monitoring.  

Responsible AI Strategy 

Although Ring emphasized all the potential benefits of AI applications in biopharma, she also highlighted the need for developing responsible and adaptive strategies.  

“Responsible AI strategy is paramount as any organization embarks on this. It's critical to not only put in a strategy but think about it differently,” said Ring. 

She emphasizes that responsible AI is not about having a governance board approve a one-time protocol. 

“This is more about a continuum because this isn't a one-time effort. The industry is dealing with an evolving model that we're constantly tuning and training,” she added. “So we not only need to think about how to monitor and govern it, but we need to think about who's governing it in a very different way.” 

Standard governance is insufficient for technology changes. Developing governance systems requires considering regulations, legal implications, talent, policies, and procedures. 

As AI advances, life sciences companies and industry leaders must prepare to leverage the technology by developing malleable strategies to regulate and incorporate them into company practices.