Anself Dynamics is an Indian health-tech startup redefining preventive healthcare through AI-driven, cost-efficient medical devices built for scale. The company designs advanced diagnostic technologies that are simple to operate, economically viable, and deployable across clinics, community centers, and remote settings. By prioritizing accessibility and usability, it aims to reduce systemic healthcare burdens while strengthening early detection and timely intervention by leveraging AI.
Its innovation portfolio reflects this mission with precision. Ocellux enables AI-powered eye screening; SLIT PAL digitizes slit lamp examinations with intelligent analytics; ThermalLook applies thermal imaging for rapid assessment; and DigiMach advances AI-based microscopy for high-accuracy diagnostics. In addition, the company develops portable corneal topography solutions to expand access to specialized ophthalmic care. Together, these platforms integrate artificial intelligence with practical medical engineering to deliver scalable, real-world impact.
National recognition underscores this momentum. The IndiaAI Mission named Anself Dynamics among India’s Top 30 AI startups, while multiple government initiatives and incubators continue to support its growth. Operating from Vadodara, Gujarat, the company advances a focused mandate: translate AI research into deployable healthcare infrastructure that delivers measurable outcomes.
At the India AI Impact Expo 2026, Devansh Parikh, Founder and Director of Anself Dynamics, spoke exclusively with The Interview World. He detailed the company’s AI architectures and model accuracy benchmarks, outlined commercialization timelines, and discussed forthcoming innovation layers. Moreover, he articulated a structured expansion roadmap designed to accelerate adoption across domestic and global markets. The following are the principal insights from that conversation.
Q: Could you provide a detailed overview of the AI-driven healthcare solutions offered by Anself Dynamics, including their core use cases?
A: Anself Dynamics aims to democratize healthcare through accessible, intuitive, AI-powered medical devices. We concentrate primarily on retinal imaging because the retina offers a unique diagnostic advantage. It is the only site in the human body where clinicians can directly visualize blood vessels non-invasively. Consequently, it serves as a critical window into systemic health and enables early detection of multiple conditions.
To operationalize this insight, we developed a smartphone-based fundus camera system. This portable platform captures high-resolution retinal images and applies AI algorithms to identify early pathological markers. Most notably, it detects diabetic retinopathy at its earliest stages.
Diabetic retinopathy occurs when prolonged hyperglycemia damages retinal blood vessels, impairs perfusion, and progressively blurs vision. If clinicians fail to detect it early, the condition advances to irreversible vision loss. Therefore, early screening is not optional; it is the only effective mitigation strategy.
India currently faces a large and growing diabetes burden, yet screening rates remain inadequate. As a result, many patients present only after significant damage has occurred. We addressed this gap by designing a solution that moves toward the patient rather than requiring the patient to access specialized facilities. The device is highly portable and easy to operate. Within 30 seconds, it generates a structured diagnostic report indicating whether diabetic retinopathy is present, the severity level, and the appropriate referral pathway.
In parallel, we developed a thermal imaging system integrated with multispectral capabilities for oral cancer screening. This platform captures detailed images of the oral cavity and surrounding lymphatic regions. It identifies early indicators such as lymph node involvement and inflammatory changes in the neck. By combining thermal data with spectral imaging, the system enhances early detection accuracy. We are presenting these integrated AI-driven screening solutions at the India AI Impact Summit to demonstrate how scalable technology can strengthen preventive oncology and ophthalmic care.
Q: Which LLMs or SLMs power your platform, and what empirical evidence supports their claimed accuracy?
A: We deploy a DenseNet architecture to power our diabetic retinopathy screening platform. Specifically, we trained the model on approximately 88,000 annotated retinal images. By leveraging the pattern-recognition capabilities of Convolutional Neural Networks (CNNs), the system learns hierarchical feature representations that distinguish subtle pathological variations. As a result, the model currently achieves up to 95% diagnostic accuracy in detecting diabetic retinopathy.
However, model generalization depends heavily on data diversity. Clinical imaging characteristics vary across geographies due to demographic, genetic, and environmental factors. Therefore, region-specific datasets are essential to further optimize performance.
Accordingly, our next phase focuses on large-scale deployment across India. We will use these deployments to capture high-quality, region-specific retinal images and continuously retrain the model. This iterative learning approach will strengthen robustness, reduce bias, and incrementally improve diagnostic accuracy in real-world settings.
Q: Has the product been commercially launched, or is it still in pre-launch? If not yet launched, what is your planned go-to-market timeline and strategy?
A: We have not yet commercially launched the product because we currently operate under a CDSCO test license. Consequently, we must complete regulatory validation before full-scale market entry. In parallel, we require structured commercialization and manufacturing support to transition from prototype to production-grade deployment.
We are securing this support through targeted grant programs under the IndiaAI Mission and the Ministry of Electronics and Information Technology (MeitY). These initiatives provide both financial backing and institutional enablement. With this support, we have developed and demonstrated a system engineered specifically for deployment in the Indian healthcare ecosystem.
Looking ahead, we will advance decisively toward commercialization next year. We will finalize regulatory clearances, scale manufacturing capabilities, and initiate structured market rollout. This phased approach ensures compliance, scalability, and operational readiness before full market penetration.
Q: What major innovation layers are you planning to develop over the next five years?
A: India has already emerged as a leading destination for medical tourism. We now aim to elevate that position by transforming the country into a global hub for high-quality clinical datasets. Indian hospitals and physicians manage an unparalleled breadth and complexity of cases. Consequently, they generate uniquely diverse and diagnostically rich data that few other healthcare systems can match.
We intend to systematically capture, curate, and document this data at scale. We will then use it to train advanced AI systems capable of predicting, stratifying, and supporting diagnosis across a wide spectrum of conditions. By embedding this intelligence into deployable platforms, we can extend India-trained AI models to healthcare systems worldwide.
In doing so, we will not only export AI-enabled diagnostic capability but also reinforce India’s leadership in clinical expertise. As global providers adopt AI systems trained on Indian datasets, they will increasingly recognize the depth of India’s medical proficiency. This, in turn, will strengthen international trust and further accelerate medical tourism into India, creating a virtuous cycle driven by data, technology, and clinical excellence.
Q: Could you outline your strategic vision for market expansion and long-term growth?
A: We are currently headquartered in Gujarat. However, we are actively expanding our international footprint. We plan to deploy our systems in South Africa, and we have already formed strategic partnerships with organizations conducting large-scale screening programs there. Through these collaborations, we will validate our platform in diverse clinical environments and demonstrate that our solutions extend beyond India to serve global markets.
Simultaneously, we are advancing our research agenda with support from the MIT Media Lab. This collaboration strengthens our innovation pipeline and reinforces our R&D-first operating model. We prioritize rigorous research, iterative validation, and evidence-based deployment. By combining global partnerships with sustained research investment, we aim to make healthcare screening consistently affordable, clinically reliable, and operationally simple, regardless of geography.
