From Cities to Villages: How Technology is Democratizing Healthcare in 2025
After completing studies in the US, Shubhang initially worked on a healthcare product design project targeting resource-constrained nations. Upon gaining some experience in product research and development in India, he is now working full-time on solving the challenges of rural healthcare by leveraging technologies.
From bustling cities to the most remote villages, tools like telemedicine, AI diagnostics, and e-clinics are bridging gaps that seemed insurmountable previously. However, as 2025 unfolds, these advancements come with their own set of challenges. While technology holds the promise of democratizing healthcare, the journey to truly inclusive, effective solutions is far from straightforward.
Telemedicine: Need to Rethink Towards Holistic Care
In a country with almost 820 million active internet users and counting, telemedicine has become a lifeline for many, offering unprecedented access to healthcare. Ideally, telemedicine would serve as a comprehensive platform, enabling seamless doctor-patient interactions for accurate diagnoses, treatment plans, follow-ups, and even preventive care. Unfortunately, the reality paints a more fragmented picture. Currently, telemedicine platforms are predominantly geared towards prescription refills and medication management.
This narrowed focus is a direct consequence of the funding winter that has hit the healthcare sector harder than most industries. Startups, under pressure to demonstrate quick returns, have gravitated toward digital pharmacy models as a viable revenue stream. The involvement of industry giants further intensifies the competition, leaving little room for smaller players to innovate on fronts like user experience. Consequently, telemedicine consultations are often reduced to mere gateways for generating valid prescriptions, overshadowing their broader potential.
Concerningly, this over-reliance on prescription-based interactions is eroding trust in the ecosystem. For instance, aggressive follow-ups for medication refills and concerns about the misuse of sensitive patient data have created skepticism among users. Moreover, associating telemedicine solely with transactional services risks undermining its credibility and alienating users who seek holistic care.
AI Diagnostics: A Revolution in Radiology
Artificial Intelligence (AI) continues to revolutionize healthcare, with radiology emerging as one of its most transformative applications. In 2024, AI demonstrated remarkable progress, nearing human-level performance in interpreting X-rays, MRIs, and CT scans. These advancements are not just enhancing diagnostic accuracy but are also paving the way for significant productivity gains. By integrating AI assistance features into diagnostic equipment, manufacturers are enabling radiologists to process more cases with improved precision, saving critical time in high-demand settings.
However, the future of AI in radiology demands more than just technological advancement; it requires contextual adaptability. A crucial next step is deploying these AI-powered tools in resource-constrained environments, such as rural clinics and under-equipped urban centers. Doing so can provide real-world feedback essential for refining these systems. Rather than attempting to develop a one-size-fits-all solution capable of interpreting ultrasound, PET scans, and other diagnostic formats, the industry must focus on fine-tuning AI tools for specific imaging modalities where they already excel.
Despite its promise, the portrayal of AI in radiology must be approached cautiously. AI, while impressive in augmenting workflows, is far from ready to autonomously diagnose patients in real-world clinical scenarios. Overhyping its capabilities risks creating unrealistic expectations and potential misuse. Instead, the emphasis should be on how AI can complement human expertise, improving outcomes without replacing the nuanced decision-making of experienced radiologists.
The broader implications of AI in radiology are equally compelling. By addressing geographic and language barriers, AI has the potential to democratize access to specialized diagnostics. For instance, patients in remote areas could benefit from AI-assisted analysis, bridging gaps in expertise by connecting them with radiologists previously out of reach due to language or logistical constraints.
Moreover, innovation is expanding beyond radiology, with AI being applied to various stages of the patient journey. From pre-screening tools to triage systems, these specialized solutions are not only improving diagnostic workflows but also enhancing doctors’ efficiency in other areas of care delivery. Such tools could be instrumental in addressing India’s fragmented healthcare system, particularly in regions where access to skilled healthcare professionals remains a challenge. The journey ahead is promising, and with the right strategies, AI could redefine the very fabric of diagnostic healthcare.
.jpg "Shubhang Tandon, Co-Founder, Online Chikitsa Mitra")
From bustling cities to the most remote villages, tools like telemedicine, AI diagnostics, and e-clinics are bridging gaps that seemed insurmountable previously. However, as 2025 unfolds, these advancements come with their own set of challenges. While technology holds the promise of democratizing healthcare, the journey to truly inclusive, effective solutions is far from straightforward.
Telemedicine: Need to Rethink Towards Holistic Care
In a country with almost 820 million active internet users and counting, telemedicine has become a lifeline for many, offering unprecedented access to healthcare. Ideally, telemedicine would serve as a comprehensive platform, enabling seamless doctor-patient interactions for accurate diagnoses, treatment plans, follow-ups, and even preventive care. Unfortunately, the reality paints a more fragmented picture. Currently, telemedicine platforms are predominantly geared towards prescription refills and medication management.
This narrowed focus is a direct consequence of the funding winter that has hit the healthcare sector harder than most industries. Startups, under pressure to demonstrate quick returns, have gravitated toward digital pharmacy models as a viable revenue stream. The involvement of industry giants further intensifies the competition, leaving little room for smaller players to innovate on fronts like user experience. Consequently, telemedicine consultations are often reduced to mere gateways for generating valid prescriptions, overshadowing their broader potential.
Concerningly, this over-reliance on prescription-based interactions is eroding trust in the ecosystem. For instance, aggressive follow-ups for medication refills and concerns about the misuse of sensitive patient data have created skepticism among users. Moreover, associating telemedicine solely with transactional services risks undermining its credibility and alienating users who seek holistic care.
AI Diagnostics: A Revolution in Radiology
Artificial Intelligence (AI) continues to revolutionize healthcare, with radiology emerging as one of its most transformative applications. In 2024, AI demonstrated remarkable progress, nearing human-level performance in interpreting X-rays, MRIs, and CT scans. These advancements are not just enhancing diagnostic accuracy but are also paving the way for significant productivity gains. By integrating AI assistance features into diagnostic equipment, manufacturers are enabling radiologists to process more cases with improved precision, saving critical time in high-demand settings.
By focusing on holistic care, building trust through transparency, and leveraging localized solutions, the health tech sector despite its funding winter, can create a sustainable and scalable healthcare ecosystem
However, the future of AI in radiology demands more than just technological advancement; it requires contextual adaptability. A crucial next step is deploying these AI-powered tools in resource-constrained environments, such as rural clinics and under-equipped urban centers. Doing so can provide real-world feedback essential for refining these systems. Rather than attempting to develop a one-size-fits-all solution capable of interpreting ultrasound, PET scans, and other diagnostic formats, the industry must focus on fine-tuning AI tools for specific imaging modalities where they already excel.
Despite its promise, the portrayal of AI in radiology must be approached cautiously. AI, while impressive in augmenting workflows, is far from ready to autonomously diagnose patients in real-world clinical scenarios. Overhyping its capabilities risks creating unrealistic expectations and potential misuse. Instead, the emphasis should be on how AI can complement human expertise, improving outcomes without replacing the nuanced decision-making of experienced radiologists.
The broader implications of AI in radiology are equally compelling. By addressing geographic and language barriers, AI has the potential to democratize access to specialized diagnostics. For instance, patients in remote areas could benefit from AI-assisted analysis, bridging gaps in expertise by connecting them with radiologists previously out of reach due to language or logistical constraints.
Moreover, innovation is expanding beyond radiology, with AI being applied to various stages of the patient journey. From pre-screening tools to triage systems, these specialized solutions are not only improving diagnostic workflows but also enhancing doctors’ efficiency in other areas of care delivery. Such tools could be instrumental in addressing India’s fragmented healthcare system, particularly in regions where access to skilled healthcare professionals remains a challenge. The journey ahead is promising, and with the right strategies, AI could redefine the very fabric of diagnostic healthcare.
