Next wave of healthcare and life sciences will be more personalized and proactive: Axtria

Axtria Inc., an AI-first data analytics innovator transforming life sciences provider, recently announced its next generation of Axtria InsightsMAx. Powered by agentic AI, the Axtria InsightsMAx.ai fundamentally reimagines how life sciences organizations harness AI through a flexible enterprise-grade platform that accelerates experimentation, deployment, and RoI.

Navdeep (Navi) Chadha, Chief Technology Officer at Axtria, tells us more. Excerpts from an interview.

DQ: How is Agentic AI redefining AI adoption for life sciences?

Navdeep Chadha: Agentic AI is not just another incremental step. It's a paradigm shift in how life sciences organizations leverage artificial intelligence. We're seeing a move beyond traditional, siloed AI applications by empowering AI systems to execute tasks autonomously, make decisions, and learn dynamically.

Imagine AI agents that can independently manage complex clinical trial logistics, personalize drug discovery pathways by iterating on hypotheses in real-time, or even proactively identify and address supply chain bottlenecks before they impact patients.

This level of autonomy translates to unprecedented efficiency gains, accelerated timelines for critical processes, and a faster journey from scientific breakthroughs to patient impact.

Imagine Agentic AI as a skilled musician in an improvisational session. Traditional AI is like a player who only follows sheet music, never deviating. But, Agentic AI? It listens and responds dynamically to the rhythm by shifting melodies, adjusting tempo, or even playing riffs that elevate the performance.

DQ: What are the unique challenges of AI implementation in pharmaceutical environments?

Navdeep Chadha: The pharmaceutical landscape presents a distinct set of hurdles for AI adoption. The stringent regulatory environment, with bodies like the FDA and EMA demanding rigorous validation and traceability, necessitates AI solutions that are not only powerful but able to be audited and explained. 

Data privacy and security are paramount, given the sensitive nature of patient information and proprietary research data. Furthermore, biological complexity—coupled with the need for deep domain expertise—requires that AI models understand nuanced scientific contexts and collaborate effectively with human experts. 

Integrating AI seamlessly into existing, often legacy, IT infrastructure also poses a significant challenge.

DQ: How is Axtria solving these problems?

Navdeep Chadha: Axtria is at the forefront of addressing these unique challenges by building AI solutions specifically tailored for the life sciences industry. Our approach emphasizes developing transparent and explainable AI models that can meet regulatory scrutiny.

Our agentic platform, Axtria InsightsMAx.ai, solves the "last-mile problem" in AI implementation. Its enterprise-grade infrastructure, modular agents, and seamless experimentation-to-production pipeline allow pharma companies to innovate rapidly, without rebuilding their tech stack. 

The platform supports Model Context Protocols (MCP), agent orchestration, and built-in compliance guardrails, making AI practical, scalable, and measurable for the real world.

Another example of our innovation is the launch of Axtria LUCCID (LLM-based Unstructured Clinical Concept Identification). This groundbreaking GenAI-powered solution revolutionizes how clinical researchers extract and process unstructured data from electronic health record (EHR) systems. 

With Axtria LUCCID, researchers can cut the data extraction time from 500 hours per patient—to just 30 minutes. By accelerating insights from raw clinical narratives, we’re enabling better, faster decision-making.

We embed our years of pharmaceutical expertise into our AI development process. That allows us to craft solutions that genuinely understand the intricacies of drug discovery, clinical development, and commercialization. 

Additionally, our platform is engineered for seamless integration with existing systems, making the adoption journey smoother for life sciences organizations. 

DQ: What measurable business impact is the platform delivering right now?

Navdeep Chadha: The impact of Axtria's platform is not just theoretical. It's being realized by six of the 10 largest global players in the pharmaceutical industry. 

For instance, one top pharma company has leveraged our platform to accelerate their lead identification process by an estimated 20%, while another has seen a marked improvement in the efficiency of their sales force deployment, leading to increased market share. 

These are not isolated instances; they are indicative of the transformative potential our solutions are unlocking across the pharmaceutical value chain.

Axtria also helps pharma companies optimize their R&D and commercialization investments for maximum returns. While every FDA-approved drug is a breakthrough, 70% fail in the commercialization phase due to the inability to identify the right patients. 

Axtria's AI-driven software directly addresses this by enabling the mapping of patient journeys: it identifies physicians who treat these patients and facilitates connections with the right physicians to ensure patients receive the best possible treatment.

DQ: What are some future trends in AI?

Navdeep Chadha: The future of AI in life sciences is poised for even more transformative advancements. We expect greater adoption of multimodal AI, which integrates diverse data types like genomics, imaging, and patient records, to provide a more holistic view of disease and treatment response.

While we wait for this development to unfold, we are encouraged by these continuing AI advancements in critical areas of healthcare:

Efficiency and effectiveness: Scaling AI applications beyond pilot programs will achieve operational excellence. The focus will be on fully integrating GenAI into drug lifecycle management, from discovery to commercialization.

Transforming customer engagement: AI-driven frameworks will enable hyper-targeted interactions with HCPs, boosting sales impact and improving therapy adoption rates.

Patient-centric innovation: Advanced analytics will identify at-risk cohorts and develop personalized interventions, improving medication adherence and therapy outcomes.

Rare diseases in spotlight: With less than 5% of 7,000 known genetic diseases having treatments, pharma companies will prioritize niche therapies, supported by AI-enabled patient analytics and real-world data.

Optimizing clinical studies: GenAI will streamline evidence generation and report authoring, reducing timelines by up to 30%.

Agentic AI platforms like Axtria InsightsMax.ai are ushering in trends such as:

Mission-driven multi-agent ecosystems: These agents coordinate across commercial, medical, and supply chain operations.

Specialized GenAI agents: These agents are trained on proprietary clinical and market access data.

Real-time orchestration: Expanding beyond simple insights, Agentic AI now actively steers campaigns, resources, and outcomes with minimal lag.

DQ: What's next for innovation in healthcare and life sciences spaces?

Navdeep Chadha: The next wave of healthcare and life sciences innovation will be characterized by a more personalized and proactive approach to patient care. AI will play a central role in enabling precision medicine, tailoring treatments to individual patient profiles based on genetic makeup and disease characteristics. 

We will see a greater emphasis on predictive and preventative healthcare, with AI algorithms identifying individuals at high risk for certain diseases and enabling timely interventions.

Integrating AI-powered digital health tools will allow patients to take greater control of their healthcare decisions, leading to better patient outcomes and lower healthcare expenses. 

Furthermore, advancements in synthetic biology and gene editing, coupled with AI-driven analysis and optimization, promise to unlock entirely new therapeutic modalities.   

DQ: Agentic AI still has challenges, such as complexity, lack of transparency, security risks, ethical considerations, and a need for robust governance. How are these being handled?

Navdeep Chadha: Addressing the inherent challenges of Agentic AI is paramount for its responsible and effective deployment. Complexity is being tackled through modular design and the development of intuitive interfaces that allow human experts to understand and interact with AI agents.

Axtria addresses these challenges head-on by embedding transparency and traceability at the core of agent design. Every action an agent takes is logged, justified, and monitored in real time. 

In line with the industry best practices and evolving regulatory guidance, Axtria adopts a "humans-in-the-loop" approach for critical decisions. Ethical use is not an afterthought—it's engineered into the system.

DQ: How is performance monitoring being handled?

Navdeep Chadha: Continuous learning doesn't mean blind evolution. Axtria's monitoring framework includes feedback loops, performance dashboards, and drift detection to ensure agents remain aligned with business goals. 

KPIs like precision, latency, intervention success rates, and compliance scores are constantly tracked, with alerting systems in place for deviations. This ensures trust and control at an enterprise scale.

DQ: Scalability limitations pose another critical challenge in Agentic AI deployment. How is that being handled?

Navdeep Chadha: Axtria's cloud-based platforms and solutions are designed with scalability in mind, enabling rapid adaptation to growing demands and market shifts in the life sciences industry. They offer modular, scalable solutions that can evolve with client needs and support faster time-to-market for innovative healthcare offerings. 

With API-first architecture and modular agents, solutions like Axtria InsightsMAx.ai integrate seamlessly into existing pharma IT ecosystems. While respecting data privacy and compliance boundaries, the platform also supports distributed architectures that operate across data-siloed environments—an essential need in global life sciences. 

We use federated learning to scale across data-siloed environments without compromising privacy or performance.