AI in Medicine: Building the Mindset for a New Era of Healthcare
Insights from Bipin Patel, CEO & Founder
November 20th, 2025
Artificial intelligence in medicine is frequently framed as a technological upgrade — a more efficient diagnostic tool, a faster analyst, a way of optimising workflows. Yet this perspective fundamentally undervalues the transformation underway. AI is changing how we think, how we learn, and how we collaborate across disciplines in science, engineering and healthcare.
This realisation crystallised for me during a recent conversation with my business colleague, Andrew Knight. We found ourselves discussing the skills the next generation of innovators will need. The discussion centred not on tools or technology, but on mindset — particularly the ability to think in different dimensions, to view problems from multiple perspectives, and to move fluidly between contexts.
This sparked the reflections that follow. At electronRx, where we are pioneering continuous digital biomarkers for respiratory disease, these themes are not theoretical abstractions. They shape the way we work, the people we hire, and the future we are building.
A New Competency for a New Healthcare Landscape
The true potential of AI-driven medicine does not lie in narrow technical expertise. It lies in the ability to integrate multiple ways of thinking. As Andrew and I discussed, multidimensional thinking will define future leaders in healthcare and life sciences.
In practice, this involves three core capabilities:
1. Algorithmic thinking: A modern innovator must understand how models learn, how bias arises, how uncertainty behaves, and what “good” looks like in a world of continuous data. Our respiratory biomarkers at electronRx work because we interrogate every layer — the physiology, the signal, the noise, the drift, the validation.
2. Clinical thinking: Technology without clinical grounding rarely improves patient lives. We design everything around real patterns of disease: exacerbation cycles, inhaler technique, symptom variation, sleep dynamics, and environmental exposures. Clinical insight anchors our science.
3. Interdisciplinary thinking: As Andrew and I reflected, thinking in different dimensions requires movement — across biology, engineering, ethics, data science, and human behaviour. The future belongs to those who can traverse these boundaries, connecting ideas that were once siloed.
How Do You Acquire Multidimensional Thinking?
A key question we explored was: how does one develop the ability to think across dimensions at all?
The answer, we concluded, is neither mysterious nor instantaneous. It begins with exposure to different environments, disciplines, and experiences.
Working in diverse teams, engaging with unfamiliar domains, participating in international environments, solving problems far outside one’s formal training and learning to operate in ambiguity, not certainty.
These experiences stretch mental models. They help people recognise patterns in one domain that illuminate another. They encourage humility, curiosity and intellectual flexibility. And over time, they build the cognitive architecture required to navigate AI-enabled healthcare.
Continuous Learning Must Replace “Training Once”
The traditional paradigm — acquire expertise early, then practise for decades — no longer fits a world defined by continuous data and ever-evolving models. Continuous learning is the only sustainable approach.
Every year brings new sensing modalities, new regulatory expectations, new clinical evidence, new approaches to modelling physiology, new patient behaviours and digital interactions.
Implications for Universities and Health Systems
1. Curricula must integrate disciplines
2. Health systems must embrace co-creation
3. Care models must shift from episodic to continuous
Why This Matters to Our Mission
I founded electronRx to solve a structural problem in respiratory medicine: the world was relying on sporadic measurements, delayed interventions, and fragmented data. AI and continuous sensing offer a fundamentally different future — one where risk is predicted earlier, deterioration is prevented, and patients are supported continuously.
The Future We Are Building
The next era of healthcare will not be defined by those who write the most advanced algorithms. It will be shaped by those who can think with AI — who can integrate clinical reasoning, computational insight, and human understanding.
