New AI Healthcare Platforms Raise Capital
Venture funding is flowing to AI-native healthcare platforms. TERN Group just raised $33 million to build a clinical AI workforce platform for the GCC region, aiming to automate repetitive tasks and support remote diagnostics. Meanwhile, AverCare's AI-based platform, which promises automated diagnostics and patient engagement, is set for a global launch this year.
The recent $33 million funding for TERN Group was led by Notion Capital and included participation from UAE-based EQ2 Ventures, highlighting a strategic focus on the Middle East. The AI-powered platform has already onboarded over 650,000 professionals across 13 countries. TERN Group's founder, Avinav Nigam, an IIT Bombay alumnus and co-founder of the $3.2 billion valued Cars24, was motivated by a personal loss that highlighted critical gaps in global healthcare staffing. This experience led to the creation of an AI-native platform designed to address the projected global shortage of 85 million healthcare workers by 2030. The platform's AI-driven system has demonstrated significant efficiency gains, reducing hiring timelines from 6-12 months to under 10 weeks. It also boasts a 96% retention rate for the healthcare workers it places and an 88% conversion rate from resume to job offer. The investment in platforms like TERN Group reflects a broader trend in the venture capital landscape. In the first half of 2025, AI-enabled healthcare startups captured 62% of all digital health venture funding in the US, raising an average of $34.4 million per round, a significant premium over non-AI startups. For biotech SaaS companies, the integration of AI and LLMs is increasingly dependent on modern data architecture patterns like the data mesh. This approach decentralizes data ownership, allowing individual teams to manage their own data domains, which improves scalability and flexibility for AI applications. A key emerging technology in this space is the Model Context Protocol (MCP), an open standard that acts as a universal connector for AI systems to securely access and interact with external data sources. For biotech, this means LLM-based assistants can connect to domain-specific databases for literature review, clinical trial data retrieval, and drug discovery, making AI-driven insights more evidence-based. Biopharmaceutical companies are already implementing data mesh architectures to accelerate innovation. By creating a self-service data infrastructure, they are enabling faster data access and insights across disparate environments, supporting numerous use cases from drug discovery to commercialization analytics. This trend is also evident in the rapid growth of the MCP ecosystem, with major AI and cloud companies now supporting the standard. For biotech firms, this provides a governed and auditable way to connect AI agents to sensitive internal research documents and proprietary compound databases, accelerating R&D while maintaining security.
