Foster AI is an early-stage generative AI health-technology company redefining clinical documentation and care workflows. It has engineered an advanced AI medical scribe that automatically produces precise, HIPAA-compliant clinical notes, transcripts, prescriptions, and discharge summaries directly from clinician–patient interactions, voice dictation, and uploaded records. As a result, providers reclaim substantial time otherwise lost to administrative tasks.
Moreover, the platform adapts intelligently to individual documentation styles and institutional templates. It integrates seamlessly with existing electronic health record systems and functions across multiple devices, ensuring operational continuity. Designed by Stanford-trained engineers and deployed across clinics in the United States, Europe, and Asia, Foster AI’s solution reduces administrative friction, strengthens care delivery, and accelerates clinical research. Importantly, it achieves these outcomes without using patient data to train its models, thereby upholding strict data governance standards.
In an exclusive discussion with The Interview World at the India AI Impact Expo 2026, Sai Anurag M, Co-founder of Foster AI, articulates the platform’s strategic impact. He details how it compresses time-to-insight in translational clinical research, presents the quantitative accuracy benchmarks that validate research outputs, and outlines the specific clinical workflows it automates across the healthcare ecosystem. In addition, he explains how Foster AI enables scalable research in rare diseases while embedding robust ethical guardrails into its architecture. The following are the principal insights from that in-depth conversation.
Q: How does Foster AI’s platform reduce time-to-insight in translational clinical research?
A: Historically, healthcare systems have relied on manual, paper-based records. Consequently, accessing structured clinical data for second opinions, clinical trials, or research has been slow, fragmented, and often infeasible. In many cases, the required information simply was not retrievable at scale.
We address this structural limitation by deploying AI-driven systems that digitize health records at the source. Physicians can dictate notes using voice input or scan handwritten documentation. The system then converts this unstructured input into standardized, searchable digital records. As a result, data that once remained locked in paper files becomes computationally accessible.
Once digitized, the record transforms from static documentation into an active intelligence layer. For instance, patients can request second opinions supported by comprehensive data retrieval. Similarly, clinical trials can automate eligibility screening, adverse event reporting, and longitudinal monitoring. Consider a blood cancer trial: investigators must capture and report adverse events with precision and speed. Our system automates that workflow, ensuring accuracy, traceability, and regulatory compliance.
Furthermore, researchers can query large datasets directly. If a team seeks insights from histopathology reports, the platform can analyse aggregated records and generate evidence-based responses in real time. In effect, AI converts isolated clinical documentation into a scalable research infrastructure. Therefore, what was previously impractical, large-scale trials, rapid cohort identification, and data-intensive research, now becomes operationally viable.
Q: What quantitative accuracy metrics validate your research outputs, and which AI model architectures underpin these results?
A: Clinical-grade systems must undergo rigorous validation before deployment. Accordingly, our platform completed a formal clinical trial at Tata Memorial Hospital. The study evaluated 300 patients across three physicians and assessed performance against defined clinical metrics, including accuracy, completeness, and timeliness.
Importantly, the evaluation remained entirely physician-reported and independently conducted. The findings were subsequently published through the European Society of Medical Oncology, ensuring transparency and public accessibility. On a ten-point scale, the system achieved a 9.1 rating. Moreover, it demonstrated fewer errors compared to historical documentation processes, thereby establishing measurable superiority over conventional workflows.
From a technical standpoint, we could not rely on existing state-of-the-art models. Contemporary architectures lack robust mechanisms for embedding deep domain knowledge and for structuring outputs in formats aligned with clinical expectations. Physicians require precision, contextual reasoning, and standardized documentation schemas, capabilities that generic models do not natively support.
Therefore, we engineered a proprietary technology stack. We built foundational capabilities that integrate domain-specific intelligence, enforce structured outputs, and maintain clinical-grade reliability. This architectural control enables us to meet the stringent performance, compliance, and usability standards required in healthcare environments.
Q: What specific clinical workflows does your platform automate within the healthcare ecosystem?
A: Our platform actively powers disease registries and protocol development initiatives. When researchers seek to design a new treatment protocol or understand the historical trajectory of a specific disease, they require structured, longitudinal data. We provide that capability. By transforming fragmented records into analysable datasets, we enable institutions to build and query comprehensive disease registries with precision.
In parallel, we streamline the operational complexity of clinical trials. Consider a multi-center study with five participating sites, 1,000 enrolled patients, and 30 visits per patient. Each visit requires completion of a Case Report Form (CRF). This structure generates tens of thousands of data entries. Historically, teams captured and processed this information manually. Consequently, the process consumed significant time, introduced inconsistencies, and constrained scalability.
We eliminate that bottleneck. Our system automates data capture directly from clinical interactions and structured records. It populates CRFs programmatically, reduces transcription errors, and ensures standardized documentation across sites. As a result, trial operations become faster, more consistent, and audit-ready.
Furthermore, we automate adverse event reporting and safety monitoring workflows. Previously, teams reported such events manually, often with time lags that affected responsiveness and oversight. Now, the system captures, structures, and flags relevant events in near real time.
Ultimately, the impact extends beyond efficiency. Manual processes inherently limit the number of trials and analyses an organization can conduct. By contrast, automated, AI-enabled infrastructure expands capacity. It allows institutions to run more trials, analyse larger datasets, and generate insights at scale, capabilities that were operationally impractical under legacy systems.
Q: How does your platform enable large-scale research in rare diseases, particularly given the challenges of limited patient populations and fragmented data?
A: At a foundational level, we unlock clinical and research use cases that previously remained inaccessible. As a direct consequence, we expand the number and reach of clinical trials. More importantly, we democratize patient access to them.
Historically, trial awareness depended on geography and institutional proximity. A patient in Hazaribagh would rarely know about a study underway at Tata Memorial Hospital. Information asymmetry limited participation. We eliminate that barrier. By digitizing records and applying intelligent matching algorithms, we connect eligible patients to relevant trials irrespective of location.
Consider a patient in Kurnool with cancer whose current line of therapy has failed. Identifying an appropriate experimental protocol requires structured data, eligibility screening, and institutional coordination. Our system enables that pathway. It surfaces suitable trials, aligns patient profiles with inclusion criteria, and facilitates referral. Consequently, patients gain access to therapeutic options that previously remained invisible.
The impact is even more pronounced in rare diseases. India has historically lacked structured, large-scale datasets for rare conditions and underrepresented populations. We build that data infrastructure. We curate, standardize, and analyse patient records to generate evidence where none previously existed. Researchers can then conduct robust studies grounded in local epidemiology rather than extrapolated global data.
Ultimately, this technological foundation strengthens continuity of care and improves clinical outcomes. It supports the development of treatment protocols tailored to Indian populations. It accelerates research into cost-effective therapies and locally optimized drug strategies. In sum, AI does not merely automate documentation; it establishes the scalable research engine required to deliver equitable, data-driven healthcare.
Q: What ethical guardrails are embedded within your system to ensure responsible and compliant use?
A: Our technology anchors itself in the clinician’s diagnosis and documented clinical judgment. The physician remains the primary source of truth. Accordingly, the system does not override medical authority; it structures, supports, and operationalizes it.
In parallel, every application undergoes review by the Institutional Ethics Committee (IEC) before deployment. We do not bypass governance. Instead, we subject each use case to formal ethical scrutiny and regulatory evaluation. This process defines what the system may permit and, equally important, what it must prohibit.
Moreover, we embed explicit guardrails within the platform’s architecture. These controls govern data access, usage boundaries, and workflow constraints. They enforce compliance in practice—not merely in policy.
Consequently, the technology passes through a rigorous safety and oversight framework consistent with Indian clinical standards before it enters real-world environments. Only after meeting these ethical, procedural, and operational thresholds does it move into live clinical use.
