Human/AI Hybrid Clinicians: Our Only Hope for Sustaining Progress in Medicine & Healthcare

Thank you for reading NewHealthcare Platforms' newsletter. With a massive value-based transformation of the healthcare industry underway, this newsletter will focus on its impact on the medical device industry reflected in the rise of value-based medical technologies, and platform business models that are significantly transforming payer and provider healthcare organizations. I will occasionally share updates on our company's unique services to accelerate and de-risk the transition!

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Hello again friends and colleagues,

For centuries, medical knowledge has progressed through an ever-expanding understanding of health and disease. Revelations were driven by the discovery of new tools and techniques that enabled us to look deeper into the human body and to demystify its workings. We then translated this growing understanding into healthcare that improved health and relieved suffering and resulted in a doubling of life expectancy over the last century.

Diabetes is a good example of this progress. From ancient healers observing mysterious sweet-tasting urine to modern scientists unraveling genetic and molecular complexities, each era’s insights have been driven by the tools and techniques available at the time. Today, we stand on the edge of another revolution—one that promises to redefine not just how we study diseases like diabetes but how we practice medicine altogether.

Early Observations: The Mysterious Disease of Sweet Urine

In ancient Egypt, the first known reference to diabetes appeared around 1550 BCE in the Ebers Papyrus. The physicians of that time noted symptoms we would now recognize as polyuria, or excessive urination. This same phenomenon was observed thousands of miles away in India, where ancient texts described a condition called “madhumeha,” or “honey urine,” because ants were drawn to the sweetness. The ancient Greeks, too, recorded observations about diabetes, with the physician Aretaeus of Cappadocia famously describing it as “a melting down of the flesh into urine.”

For generations, these symptoms painted a picture of an unknowable, incurable condition. Without the diagnostic or investigative tools we now take for granted, early physicians could only document what they saw and speculate on the nature of the illness.

Progress through the Centuries: Connecting the Dots

Fast forward to the 17th century, when Thomas Willis, an English physician, noted that diabetic urine had a peculiar sweetness. This discovery led to the term “mellitus,” Latin for “honey-sweet.” Yet, it wasn’t until the 19th century that significant progress began to unfold. In 1869, German scientist Paul Langerhans discovered clusters of cells in the pancreas, which would later be understood as insulin-producing beta cells. The work of Joseph von Mering and Oskar Minkowski in 1889, showing that removing the pancreas from dogs induced diabetes, linked the organ to the disease definitively. And in the 20th century, the groundbreaking work of Frederick Banting and Charles Best culminated in the isolation of insulin in 1921, a discovery that transformed diabetes from a death sentence to a manageable condition.

Our modern understanding of diabetes is far more nuanced. We now know that Type 1 diabetes is an autoimmune disease where the immune system attacks the beta cells in the pancreas, while Type 2 diabetes involves a complex interplay of insulin resistance, genetics, and lifestyle factors. These discoveries have been underpinned by breakthroughs in cellular biology, biochemistry, and genetics—all thanks to the evolving arsenal of scientific tools.

New Techniques Redefining Research

Let's look at some of the new tools and techniques that will drive the next phase of progress in medicine.

Spatial Transcriptomics (ST)

One of the most revolutionary tools to emerge in recent years which allows researchers to map gene expression within specific regions of tissue samples, offering a clear view of how different cells behave in their native environment. Imagine the ability to pinpoint exactly where and how pancreatic beta cells are damaged or lost in diabetes. The ability to identify these microenvironments and understand their behavior could unlock answers to long-standing questions: Why does the immune system attack these cells in Type 1 diabetes? How do early signs of insulin resistance manifest in Type 2 diabetes?

The Power of Single-Cell Analysis

Single-nucleus RNA sequencing (snRNA-seq) is another cutting-edge technique that has deepened our comprehension of cellular diversity. Unlike traditional RNA sequencing, which looks at bulk tissues and averages out data, snRNA-seq allows scientists to analyze individual cell types within complex tissues. This could reveal, for example, subtle differences between healthy beta cells and those under autoimmune attack, providing insights into why some cells are more resilient than others.

3D Tissue Reconstruction

Techniques that allow for 3D tissue reconstruction take this understanding a step further. By co-registering serial sections of tissue, scientists can visualize the three-dimensional structure of the pancreas and its cellular interactions. This 3D mapping could help identify how immune cells infiltrate pancreatic islets or how fibrosis develops in insulin-resistant individuals. Such spatial context is invaluable; it shows us not only which cells are involved but how they are arranged and interact in living tissue.

Integrative Approaches for a Complete Picture

Integrating these techniques opens up even broader possibilities. A multi-modal approach, combining spatial transcriptomics, snRNA-seq, and other imaging technologies, can provide a comprehensive map of cellular interactions in the pancreas. For diabetes research, this could mean identifying new therapeutic targets or early biomarkers that could lead to earlier diagnosis and more effective treatments.

The Expansion of Knowledge and Specialization

As we reflect on how new tools like these have driven the expansion of medical knowledge, it’s clear that this surge has had far-reaching consequences. The growth in understanding led to specialization in medicine. Endocrinologists emerged as experts in hormones, metabolism, and diseases like diabetes. But as medical knowledge deepened, even more subspecialties arose—pediatric endocrinologists focused on children, diabetologists specialized further in diabetes care, and so forth.

Specialization has undoubtedly improved patient care, allowing doctors to gain a profound understanding of their specific fields. Yet, it comes with its own set of challenges. The ever-increasing volume of information makes it impossible for a single physician to master all aspects of even their subspecialty. What happens when the tools become so advanced, the data so granular, that it becomes unmanageable. 

A Glimpse into the Future: AI and Medicine

We’re already seeing signs of this. Techniques that map genetic variations, monitor cellular interactions, and explore tissue in 3D have applications not just in diabetes but across all diseases. Each advance adds a layer of complexity that demands new forms of expertise. It’s not hard to imagine a world where, if we continue on this path, hundreds of micro-specialties emerge, each focusing on ever-narrower slices of medical science. But this model would be unsustainable. Human cognitive limitations mean that physicians can only specialize so far before it becomes unfeasible to maintain a comprehensive understanding of their field.

This is where we must envision a new model for medicine—one where human expertise is augmented by artificial intelligence (AI). AI systems excel at processing vast quantities of data and identifying patterns across multiple sources. While physicians excel in empathy, judgment, and the subtle art of patient care, AI can support them by handling the heavy cognitive load that comes with modern medical practice.

The Hybrid Model of Medicine: Division of Labor

In this hybrid model, physicians would focus on patient interactions, diagnoses, and treatment plans, guided by their clinical expertise and informed by AI’s deep data analysis. AI would sift through genetic profiles, metabolic markers, and environmental factors to flag potential issues and suggest courses of action. Together, this combination could ensure that every patient receives a care plan tailored to their unique biological makeup, informed by the most cutting-edge research available.

Imagine a future where an endocrinologist reviewing a patient’s case receives real-time insights from an AI trained on millions of diabetes-related data points. The AI could highlight potential genetic markers associated with a higher risk of Type 1 diabetes or identify lifestyle factors that might contribute to insulin resistance in Type 2 cases. The physician, armed with this analysis, could then take the time to explain these findings to the patient, discuss treatment options, and develop a personalized plan. This is what has been called Precision Medicine!

But transitioning to such a model will come with challenges. Trust in AI systems, regulation of their use, and training physicians to work seamlessly alongside them will require careful planning. Yet, the potential benefits are enormous. We could shift from a system overloaded with specialists to one where generalists, supported by AI, can provide highly informed, personalized care. The age of medicine dictated by human cognitive limits would give way to one enhanced by technology, creating a new balance between breadth and depth of expertise.

Embracing the Hybrid Future

The journey from ancient descriptions of “honey urine” to today’s complex, data-driven approaches to understanding diabetes illustrates one clear truth: the expansion of knowledge is powered by the tools at our disposal. As we invent new methods to see more deeply into the body, our understanding expands and becomes more intricate. In response, the practice of medicine must evolve, moving beyond the traditional models of specialization to a new era where human intelligence and artificial intelligence collaborate to provide the best possible care. This partnership between human and machine may soon redefine what it means to be a physician—and what it means to be cared for as a patient.

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See you next week,

Sam