As pharmaceutical research and development (R&D) continues to evolve at a rapid pace, AI assistants—software powered by artificial intelligence (AI) that responds to inquiries in human-like text or voice—hold the promise of significantly enhancing efficiency. These tools claim to support tasks like data analysis, due diligence, risk assessment, and even clinical translation.
However, the full potential of AI assistants in preclinical R&D remains largely untapped. As discussed in a previous blog post, while a robust data foundation is essential, data and machine-learning algorithms alone aren’t enough. To truly empower scientists, AI assistants must seamlessly integrate into their complex workflows, offering both the conversational ease of generative AI (GenAI) and the analytical power of traditional graphical user interfaces (GUIs).
Building trust with scientists is crucial in this endeavor. At BenchSci, we understand that trust isn't just a byproduct of technology; it's earned through a deep understanding of scientists' needs, meticulous design, and relentless refinement. This blog post highlights the importance of tailoring AI assistants to pharma scientists' workflows, demonstrating how BenchSci’s user-centric approach merges GenAI with intuitive GUIs to create a transformative tool that advances research and builds trust.
Beyond Chat: Scientists’ Workflows Demand More Than Simple Conversations
From the initial discovery phase to post-market surveillance, pharmaceutical scientists navigate intricate workflows where data analysis, experiment tracking, therapeutic area knowledge, project phase, and collaboration are all interconnected. These intertwined factors influence how an AI assistant interprets a scientist's intent, ultimately affecting what information surfaces and how it's presented within the chat interface. The complex landscape of pharma R&D demands AI assistants that can do more than just chat. While advancements in AI, like the incorporation of "thinking" steps in ChatGPT, have improved reasoning capabilities, generic tools still struggle to grasp the nuances of scientific inquiry, especially when used out of the box. They might offer quick responses, but without direct links to the evidence behind their claims, they fail to inspire the trust that scientists need to fully embrace AI as a research partner.
To truly empower scientists, AI assistants must seamlessly integrate into their workflows, mirroring their mental models—the way they approach problems, dissect data, and arrive at conclusions. This means providing accurate information and clear explanations of the AI's reasoning and the sources it used. This transparency is crucial for building trust and fostering adoption, especially in a field where evidence-based decision-making is paramount. Specialized AI partners should not only understand the unique challenges scientists face but also offer a cohesive continuum of support from discovery to market, all while providing clear explanations and references for their insights.
This is where tailored AI solutions become indispensable. These solutions understand scientists’ unique needs and challenges and integrate directly into their workflows. But it’s not just about data foundations, which we’ve discussed in a previous blog. It’s also about providing intuitive GUIs, employing user intent models (algorithms that predict what a user is trying to achieve), enabling seamless collaboration among team members, and offering tailored experiences that empower scientists to navigate the intricate landscape of pharma research with confidence and efficiency.
There are several challenges that impact the development of effective specialized AI assistants for pharma, including:
- Deep Domain Expertise: Understanding pharma research nuances requires deep expertise in scientific principles, experimental methods, regulatory frameworks, and organizational cultures. This level of specialized knowledge is often absent in the development of generic AI tools that do not focus on specific domains. Beyond that, the disease and therapeutic mechanisms being studied, the stage of the drug development pipeline, and the specific role of the scientist all have a significant impact on the design and effectiveness of AI solutions.
- Data Accessibility and Quality: The siloed nature of pharmaceutical data, often scattered across different systems, hinders the access and integration of large, high-quality datasets needed for effective AI model development.
- Adaptability through Continuous Improvement: As scientific research and drug discovery constantly evolve, AI assistants must keep pace. This necessitates significant, ongoing investment in testing, validation, and refinement by dedicated AI development teams.
- Cross-functional Collaboration and Expertise: Building successful AI tools requires collaboration among scientists and domain experts, engineers, user researchers, product designers, and product managers to align technical capabilities with user needs—a challenge for organizations with segmented teams or limited resources.
- Significant Capital Investment: Developing tailored AI solutions for the specialized pharma field requires substantial capital investment. This is due to the complexity of the technology involved and the notoriously long sales cycles in the pharma industry, which can delay revenue generation.
Consider a pharma scientist immersed in target identification, sifting through mountains of research papers and genomic data. They turn to a generic AI assistant, hoping to quickly pinpoint relevant studies and extract key insights. However, the AI, untrained in the nuances of biomedical terminology and research methodologies, struggles to interpret their complex queries. It delivers a surge of irrelevant information or fails to grasp the specific context of their research. Frustrated and pressed for time, the scientist resorts to manual literature reviews, sacrificing valuable hours that could be spent on experimentation and discovery.
This disconnect between generic AI solutions and scientists' specific needs has significant consequences. It can erode trust, breed skepticism, and hinder broader AI adoption in pharma. When tools fail to comprehend scientific terminology, specialized language, workflows, or the unique challenges faced by scientists, they are less likely to be trusted for critical decision-making in the scientific community.
BenchSci’s Approach: Merging the Best of Both Worlds
In the complex world of pharmaceutical R&D, AI assistants must evolve beyond mere tools. They must be trusted partners, seamlessly integrating into scientists’ intricate workflows and offering intuitive, user-friendly interfaces that foster adoption and empower confident decision-making. Generic AI solutions often fall short in this domain due to their lack of specialized knowledge and inability to interpret nuanced scientific queries, leading to frustration and distrust. Building trust with scientists who uphold rigorous standards for evidence and accuracy requires transparency, evidence-backed conclusions, and an intuitive user experience that facilitates understanding through clear visualizations.
It’s clear that science is not 'one size fits all,' and we've taken these learnings to heart in building our platform, ASCEND. ASCEND seamlessly integrates the power of GenAI with intuitive GUIs and robust collaboration tools, creating a multi-modal experience built on trust. With ASCEND, researchers can ask complex questions, and explore, analyze, and visualize data, all while receiving clear explanations of the AI's insights.
At the heart of ASCEND's design is a deep understanding of the intricacies that underlie scientists' daily research, including the challenges they face and the collaborations that drive progress. We've prioritized creating a tool that not only leverages the power of GenAI but also seamlessly integrates into existing workflows, becoming an indispensable part of their research toolkit. ASCEND is adaptable to the unique goals and technical proficiencies of each individual, from bench scientists to project leaders.
Unlike generic AI assistants that rely solely on text, ASCEND leverages graphical elements specifically designed to support scientists' needs. These include tailored charts that provide detailed data breakdowns, dynamic visualizations, and specialized tools for manipulating complex datasets directly within the interface. These features go beyond basic textual responses, enabling researchers to do a deep analysis and allowing them to make data-driven decisions with greater confidence.
To foster trust, ASCEND prioritizes transparency and explainability. It's not enough for AI to simply provide answers; it must also clearly articulate the reasoning and evidence behind those answers, much like how scientific journals present data with supporting figures and tables. ASCEND achieves this by providing clear documentation, contextual explanations for its insights, and an intuitive user interface designed to facilitate understanding and empower scientists to critically assess results and make informed decisions.
Furthermore, ASCEND's GUI empowers scientists to efficiently filter and sort through vast datasets based on relevant criteria, enabling them to quickly identify promising leads. The GUI seamlessly integrates dynamic visualizations that can be manipulated in real time, fostering a deeper understanding and exploration of complex results.
Finally, our platform provides access to a comprehensive database, allowing multiple users to work independently and collaboratively, accelerating research. These unique features allow ASCEND to bridge the gap between exploring data availability and engaging in a conversational experience, offering scientists a streamlined path to discovery.
Our Continued Investment In User Research
We believe the most effective pharma AI assistants are built hand-in-hand with the scientists who will use them. Our platform, ASCEND, exemplifies this approach, with over 100 of our internal scientists working alongside our engineers to ensure we have a solution that truly understands and addresses the most pressing challenges that scientists face.
User research is the cornerstone of our product development. From day one, we’ve invested heavily in understanding scientists’ needs, building a large dedicated team of user researchers and product designers whose diverse expertise bridges the gap between complex AI technologies and scientists’ needs. Beyond our scientific team, our collaborative development process incorporates insights from across BenchSci. This includes crucial input from our engineering teams, product managers, AI specialists, and our account teams, who all play a vital role in shaping our AI solutions.
This commitment to understanding our users is an ongoing journey. We've adopted a continuous discovery and validation (CDV) methodology that involves ongoing collaboration and communication with our users and scientists. Over the years, we’ve interviewed thousands of scientists from the pharma industry and academia. This ongoing dialogue ensures we stay attuned to their evolving needs. Our deep internal scientific expertise then allows us to go beyond surface-level feedback, identifying the root challenges and opportunities for impactful solutions. We also adapt our CDV process to each customer, ensuring we understand the unique needs of their scientific teams before every deployment.
Pre-deployment CDV with each pharma organization allows us to tailor our solutions precisely. This is crucial for several reasons:
- Relevance and Impact: Through in-depth interviews and usability testing, we gain invaluable insights into the unique workflows and needs of scientists at each organization, ensuring our products directly address their most pressing challenges.
- Continuous Improvement: Regular feedback loops with users empower us to iteratively refine our tools based on real-world experiences, guaranteeing ongoing evolution and enhancement.
- Efficient Adoption: Close collaboration with scientists streamlines the integration of our AI Assistant into existing workflows, minimizing disruption and maximizing value.
Our collaboration with Novo Nordisk exemplifies the power of this user-centric approach. During a 2023 pilot program, we collaborated closely with their preclinical teams to tailor ASCEND to their specific needs in target risk assessment and biomarker identification. This resulted in a 60 percent increase in productivity, underscoring the benefits of tailoring AI solutions to specific needs. Following the pilot's positive outcomes, ASCEND has become an integral part of Novo Nordisk's preclinical research, empowering their teams to make faster, more informed decisions and ultimately accelerate the drug discovery process.
Our commitment to CDV has never wavered, but our approach has evolved over time. In addition to pre-deployment CDV, we’ve established a dedicated pool of scientists who participate in regular touchpoints throughout the development cycle. This structured approach has allowed us to engage with more scientists more efficiently. Recognizing the need for ongoing improvement, we continuously refine our CDV process. By tracking metrics such as the speed of translating insights into enhancements, community engagement, and feedback quality, we adapt our platform to better serve scientists. This data-driven strategy optimizes our processes, amplifies scientists’ contributions, and ensures our AI assistant is finely tuned for their success.
This user-centric approach has fostered trust and allowed us to build an AI Assistant that is technically sound and meets scientists’ needs for scientific accuracy, practical usability, market relevance, and customer success.
The Future of AI in Pharma
The future of drug discovery rests on leveraging the power of AI to accelerate research and bring life-saving treatments to patients faster. However, for AI to truly revolutionize pharma, it needs to evolve beyond a static algorithm. It needs to be an interactive, trusted partner for scientists, seamlessly integrating into their complex workflows and providing intuitive, user-friendly interfaces.
We believe in the power of human-centered design. Our platform, ASCEND, exemplifies this commitment by merging the best of both worlds—GenAI and intuitive GUIs—to create a truly comprehensive solution for scientists. By prioritizing user needs and CDV, we are building an AI assistant that empowers scientists to navigate vast datasets, uncover hidden patterns, and ultimately accelerate breakthroughs in drug discovery. We envision a future where AI-powered tools like ASCEND not only transform the way research is conducted but also significantly reduce the time and cost of bringing new therapies to market, improving the lives of patients worldwide.
