New Clinical Research Division Aims to Modernize Trial Management
Qtis.ai has officially launched its new Clinical Research Division and introduced a commercially available AI-native Clinical Trial Management System (CTMS), marking a major expansion into the rapidly growing clinical research technology market. The company also announced that Cardiac Dimensions, Inc., a medical device company headquartered in Kirkland, has become the first commercial customer to deploy the platform.
The launch positions Qtis.ai in one of healthcare’s fastest-growing software segments. Industry estimates from Grand View Research indicate that the global Clinical Trial Management System market, valued at approximately $2.4 billion in 2025, is expected to grow to nearly $7.4 billion by 2033. The expansion is being driven by increasing clinical trial complexity, stricter regulatory oversight, and rising demand for digital infrastructure capable of supporting modern research operations.
A New Generation of Clinical Trial Technology
Qtis.ai’s new platform was developed specifically for the artificial intelligence era rather than adapted from older software architectures. According to the company, the CTMS was built from the ground up using a unified AI-native design powered by its proprietary language model, LORIS.
Unlike traditional CTMS platforms that primarily function as transactional databases, the new system allows clinical teams to interact with the platform using natural language queries and commands. The goal is to simplify complex workflows while preserving operational controls, auditability, and regulatory compliance requirements.
Mark Swartz, Chief Executive Officer of Qtis.ai, said the company believes clinical research infrastructure must evolve beyond legacy software models.
“Clinical research remains one of the most data-intensive industries in the world, yet many of the systems supporting it were architected decades before artificial intelligence became practical,” Swartz said. “Modern clinical trials routinely generate hundreds of thousands, and often millions, of data points across sites, investigators, sponsors, CROs, and regulators. We believe the next generation of clinical research infrastructure must be designed around intelligence from inception rather than attempting to retrofit AI into legacy architectures.”
First Commercial Deployment with Cardiac Dimensions
The first commercial implementation of the platform will be carried out by Cardiac Dimensions, which develops minimally invasive catheter-based technologies for the treatment of heart failure and functional mitral regurgitation. The company operates clinical programs across multiple international markets, making it a significant early validation partner for the new system.
Hank Hauser, Vice President of Global Clinical Affairs at Cardiac Dimensions, said the platform stood out during the evaluation process.
“It is one of the most intuitive CTMS platforms our team has evaluated,” Hauser said.
Qtis.ai noted that the deployment follows a successful pilot validation phase and represents the company’s first revenue-generating implementation within its Clinical Research Division.
Why Legacy CTMS Platforms Face Growing Pressure
Clinical trial management systems have become essential tools for coordinating study timelines, site activities, patient enrollment, monitoring visits, regulatory documentation, and financial tracking. However, many existing platforms were originally designed long before AI-driven automation and advanced analytics became viable.
As sponsors and contract research organizations attempt to add modern AI capabilities to older systems, they often encounter challenges such as:
- Fragmented data architectures
- Multiple integration layers
- Separate AI modules and workflows
- Inconsistent user experiences
- Higher implementation and maintenance costs
Qtis.ai argues that building AI directly into the core platform eliminates much of this complexity and enables a more seamless experience for clinical operations teams.
Addressing AI Governance in Regulated Environments
One of the most significant barriers to AI adoption in clinical research is regulatory compliance. Systems operating under GxP standards must maintain strict controls related to traceability, validation, documentation, change management, and audit readiness.
Many healthcare organizations have expressed concerns that AI tools added onto legacy platforms may create compliance risks if governance mechanisms are not deeply integrated into the system architecture.
To address these concerns, Qtis.ai said its platform was designed with GxP-ready deployment capabilities from the outset. Key features include:
Model governance controls
Version management for AI models
Comprehensive audit trails
Human oversight mechanisms
Validation and change-control processes
Swartz emphasized that AI governance cannot be an afterthought in regulated industries.
“AI cannot be treated as an add-on in regulated environments,” he said. “It must be designed into the platform from day one if organizations expect to realize the benefits of automation while maintaining compliance.”
The Growing Importance of AI in Clinical Operations
The timing of the launch reflects broader changes occurring across the clinical research industry. Trials are becoming increasingly complex due to:
- Multi-country study designs
- Decentralized and hybrid trial models
- Higher volumes of patient data
- Expanded use of wearable and remote-monitoring technologies
- More stringent regulatory reporting requirements
- Greater collaboration between sponsors, CROs, and research sites
These trends have created demand for systems that can automate repetitive tasks, surface operational insights, and reduce the administrative burden placed on clinical teams.
AI-native platforms are increasingly being viewed as a potential solution for improving efficiency while helping organizations manage the growing scale of clinical development programs.
A Broader Clinical Research Infrastructure Strategy
The CTMS launch is only the first step in Qtis.ai’s larger clinical research technology roadmap.
The company plans to expand its Clinical Research Division with additional enterprise-grade solutions designed to support sponsors, CROs, medical device companies, and research institutions. Future offerings are expected to include:
- Electronic Data Capture (EDC) systems
- Document management platforms
- Integrated research operations tools
- Workflow automation capabilities
- AI-driven analytics and reporting solutions
By building multiple research applications on a common AI-native foundation, Qtis.ai aims to create a connected ecosystem that reduces data silos and improves coordination across the clinical development lifecycle.
Market Opportunity and Competitive Landscape
The clinical trial software market has attracted significant investment in recent years as pharmaceutical companies, biotechnology firms, and medical device manufacturers seek more efficient ways to manage research programs.
Established CTMS vendors have traditionally focused on workflow management and operational tracking, while newer entrants are increasingly emphasizing artificial intelligence, automation, and predictive analytics.
Qtis.ai’s differentiation strategy centers on its claim that the platform was designed as AI-native from inception rather than enhanced later with AI features. Whether this approach translates into measurable operational advantages will likely be evaluated through early customer deployments such as the implementation at Cardiac Dimensions.
Implications for the Future of Clinical Research
The launch underscores a broader industry shift toward intelligent research infrastructure. As clinical studies continue to generate larger datasets and involve more stakeholders, organizations are seeking platforms capable of handling both operational management and advanced AI-driven decision support.
For sponsors and research organizations, the potential benefits include faster study execution, improved data visibility, reduced administrative workload, and greater scalability. For technology providers, the opportunity lies in replacing or modernizing systems that were originally built for a very different era of clinical research.
Qtis.ai believes that AI-native infrastructure will become increasingly central to the future of clinical development. With its first commercial customer now announced and additional product expansion planned, the company is positioning itself as a participant in the next generation of clinical research technology platforms.
The successful adoption of its CTMS by organizations conducting complex global studies could serve as an important indicator of how quickly the industry is prepared to move from AI-enhanced software toward fully AI-native clinical research infrastructure.
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