Medicosnext
From Curiosity to Care, and Why Nepal Must Lean In Now
Dr. Neil Pande
Dr. Neil is a Kathmandu-based dental surgeon with nearly three decades of clinical experience and an early adopter of digital dentistry. His current work focuses on integrating AI into healthcare to improve diagnosis, documentation, and patient journeys, particularly in resource-limited settings. He has presented his vision at international forums and is actively developing ethical, clinician-supportive AI solutions grounded in real-world practice.
My relationship with technology has never been about being ahead of the curve. It has been about curiosity. Like many professionals of my generation, I grew up watching tools change slowly but surely, from paper files to computers, from film to digital, from memory-based systems to data-supported ones. Along the way, I tried to understand these changes rather than resist them.
In the late 1990s, when the internet was still unfamiliar to most of us, I remember experimenting with basic web design and putting together a simple healthcare-related website. It was not polished or ambitious. It was simply an attempt to explore how information could be shared beyond clinic walls. Looking back, it was less about technology and more about a habit of learning by doing. That mindset has stayed with me.
When Technology Becomes Part of Practice
As my clinical work evolved, adopting digital tools felt natural rather than deliberate. Digital imaging, CAD CAM workflows, and guided planning slowly found their place in daily practice, not because they were fashionable, but because they made work more predictable and communication clearer.
Dentistry is a field of fine margins. Small inaccuracies are felt immediately by patients. When
technology reduces variability, improves records, and supports better decision-making, it quietly
improves care. Over time, these tools stopped feeling like “technology” and started feeling like
routine clinical thinking.
From Tools to Questions
My deeper engagement with artificial intelligence grew not from ambition, but from questions. Conversations at home, especially with my son who works in technology, exposed me to how differently engineers approach problems. Watching software being built, tested, fail, and improve was eye-opening.
I experimented with simple tools, believing clinical experience alone might be enough to translate ideas into systems. It quickly became clear that it was not. Meaningful healthcare solutions need collaboration. Clinicians understand context. Engineers understand structure. Without both, systems remain incomplete.
That realisation pushed me to look more honestly at everyday clinical pain points. The Invisible Friction in Healthcare
Like many doctors, my challenge was never diagnosis or treatment itself. It was everything
around it. Incomplete histories. Poor documentation. The time lost repeating the same questions
multiple times. Patients forgetting instructions. Records scattered across paper, memory, and
disconnected systems. These gaps do not just slow us down. They directly affect outcomes.
In May, while in Boston, I shared these frustrations with two close friends and software
engineers, Baibhav Thapa and Mamta Sonwalkar of Intelladapt, Boston. I tried to explain how
much valuable clinical insight gets lost between a patient’s first phone call and their final follow-
up. That conversation marked a turning point. It shifted my thinking from “Can AI do more?” to a
more grounded question. Can AI reduce friction without removing the human doctor from the
centre?
From an Idea to a Shared Vision
That collaboration evolved into a working vision and software platform, which we presented in November at the 2nd Global Symposium on AI in Dentistry. The focus was not on showcasing technology, but on demonstrating how clinician-led AI systems can improve documentation, support diagnostic thinking, and enhance continuity of care while keeping the doctor firmly in control.
In January, I formally enrolled in an ongoing AI in Dentistry program at Harvard. Learning alongside clinicians and researchers actively shaping this field has been grounding. The most important lesson has not been about algorithms or computational power. It has been about systems, standards, and repeatability. Good AI is not about brilliance. It is about reliability.
Kendall Square and Kolti: A Difference of Access
These ideas became clearer to me while comparing between two very different places. Kendall Square in Boston represents one of the most concentrated hubs of healthcare innovation in the world. Multidisciplinary teams, layered diagnostics, structured medical records, and access to second opinions as part of routine care. Decisions there are supported by systems.
Kolti in Bajura represents something very different. Not a lack of intelligence or commitment, but a lack of access. A clinician there often works alone, with limited diagnostics and fragmented patient histories. Care depends heavily on individual experience rather than structured support.
The difference between Kendall Square and Kolti is not talent. It is infrastructure.
AI as a Practical Bridge
This is where AI has a meaningful role to play. If designed responsibly, AI can standardise history taking, prompt relevant clinical questions, flag red flags, and support evidence-based decision- making regardless of location. A clinician in a remote setting, supported by AI tools, can approach the same level of diagnostic confidence as one working in a global medical hub.
Not because AI replaces expertise, but because it provides structure where systems are missing.
Where Innovation Meets Reality
Back in Nepal, we began piloting AI tools inside real clinics, with real patients, real constraints, and real consequences. Our approach has been deliberately cautious. The AI does not diagnose independently. It listens, structures information, identifies missing data, and suggests possibilities. The final decision always rests with the clinician. This distinction matters.
AI is excellent at pattern recognition, consistency, and memory. Humans remain better at judgment, empathy, and ethical responsibility. When these roles are respected, outcomes improve. Patients feel heard. Their stories are captured properly. Trust improves. Anxiety reduces. Compliance improves.
January 2 and the Bigger Picture
These ideas came together publicly on January 2, when Upendra Devkota Memorial Foundation organised an AI in Healthcare interaction program in Nepal. The aim was not to promote products, but to start a conversation between clinicians, technologists, and policymakers.
One message stood out clearly. Nepal does not lack talent. Nepal lacks integration.
Doctors work in silos. Developers build without enough clinical grounding. Policymakers remain cautious, often for valid reasons, but sometimes distant from real workflows. AI can act as a bridge, but only if all three groups work together.
A Window Nepal Should Not Miss
Challenges remain. Data quality is inconsistent. Digital literacy varies. Regulatory clarity is still evolving. These concerns are real and must be addressed openly. AI in healthcare cannot be rushed. It must be evidence-based, transparent, and assistive.
At the same time, Nepal has a unique opportunity. With a national digital identity system already in place, AI-assisted healthcare workflows could be integrated at scale. Standardised digital histories, secure record sharing, and AI-supported triage could strengthen primary care while reducing pressure on tertiary centres.
AI will not fix broken systems on its own. But used wisely, it can reduce noise, improve clarity, and allow healthcare professionals to do what they do best.
Care
The future of healthcare will not be built by AI alone. It will be built by humans who know how to use it thoughtfully. And if we choose that path, the distance between Kendall Square and Kolti does not have to determine the quality of care anymore.
AI is here to amplify our care as healthcare professionals and not replace us. It is here to free us to be more connected to our patients and heal them better.
