By:Daniel Cabrera, Felix Ankel
Our relationship with knowledge, professional identity, and structure is changing exponentially. This is the second part of a three-part series that describes a potential future relationship between AI, human capital, and learning organizations. In our previous post, we discussed the limitations of AI as a tool. In this post, we will discuss human capital.
About Chefs
For generations, medical education has centered around training individuals to own knowledge. We built entire systems of credentialing, assessment, and professional identity, assuming that expertise is defined by how much you know and how quickly you can recall it. Prestige came from encyclopedic recall. And clinical authority was, in large part, a reflection of the depth of an internal knowledge base. This model made sense in an era where access to information was limited, and the clinician was the repository of expertise. That era is gone.
Today, we find ourselves in a radically different landscape, one where intelligence, especially in the form of artificial intelligence, is abundant, inexpensive, and ever-present. AI models can synthesize vast amounts of data, generate differential diagnoses, and even draft patient notes. The value proposition is no longer owning knowledge but knowing how to manage, interrogate, and co-perform with intelligence systems. The future clinician must not only understand the content but must know when, why, and how to use AI, and equally important, when not to. This shift demands an entirely new set of educational priorities, where fusion skills like judgment, explainability, collaboration, and critical thinking take center stage.
This is more than a curricular update, it’s a transformation of identity. We are no longer training people to be walking databases. We are training them to be stewards and administrators of intelligence, capable of navigating a landscape where humans and machines think and act together. In this world, success is not measured by what you know, but by how wisely you manage knowledge systems, how ethically you apply them, and how human you remain in the process. It’s time for our learning environments, assessments, and cultural norms to reflect that reality. We are not just building better doctors—we are cultivating adaptive intelligence managers for a polyintelligent future. We are, in a way, creating Chefs. Who can gather the ingredients, understand what goes well with something, understand what the customer wants, have the expertise to put together and create an experience (navigate) a fantastic meal (or a medical experience).
We create chefs, teaching them Fusion Skills.
Working with AI does not require an advanced degree in informatics just like performing basic bedside ultrasonography does not require an advanced fellowship. However, as with any adjunct, the bedside clinician needs to have several skills and roles to be effective
The understander
Clinicians need to have a basic understanding of AI. They must understand the basic concepts of inputs/black box/outputs. They need to understand the potential of bias in inputs, algorithms, and outputs.
The explainer
Clinicians need to be able to explain AI recommendations, just as patients receive discharge recommendations.
The collaborator
Clinicians need to be able to collaborate with both machines and patients, families, members of the health care team, and the community. This may require more inquiry and fewer declarative statements.
The advocate
Clinicians need to have the patient’s interest front and center. An algorithm may suggest a complex chemotherapy treatment plan for a patient to extend their life. The patient may choose to go home to be with their loved ones without a treatment regimen with severe side effects. Clinicians will need to advocate for patients’ desires even if in contradiction to AI recommendations.
The skeptic
Clinicians must have impeccable situational awareness to understand the limitations of AI. Suppose AI-generated predictive analytics suggest a high probability of sepsis and the need for antibiotics, trained on algorithms without a high degree of viral illness in the community. In that case, clinicians must be able to pivot away from rapid broad-spectrum antibiotics in an environment with high viral illness.
More Human, Less Machine: The Artisan Future of Clinical Practice
Think about AI-driven or human-driven practice as the difference between a mass-produced fast-food meal and a dish prepared by an experienced chef. Both satisfy a need, but only one carries the signature of craftsmanship, presence, and care. AI might offer efficiency, but it lacks presence. It can generate text, but it can’t read a room. It can recognize patterns, but it can’t hold a patient’s hand in fear, navigate a difficult conversation, or draw from years of lived clinical wisdom to choose the path that feels right, not just optimal. In the age of co-intelligence, the premium shifts from the algorithm to the artisan—from the output to the origin of care.
This is where clinicians will thrive. The skills defining future excellence are deeply, irreducibly human: compassionate communication, comfort with ambiguity, emotional management, conflict resolution, shared decision-making, and procedural nuance. These are not “soft” skills; they are the hard currency of trust, healing, and meaning. While AI democratizes knowledge, clinicians must embody wisdom. The future is not man or machine, it is man with machine. But the center of gravity, the source of value, shifts back to the human. Ultimately, it’s not just about being accurate; it’s about being present.
Moving from morning to afternoon subjects
Traditionally, morning classes have been based on the natural sciences, such as biology, chemistry, and physics; afternoon classes consisted of psychology, sociology, economics, management, communication, and political science. The more human, less machine skills needed for tomorrow will be based more heavily on afternoon subjects. What medical schools and residencies focus on the afternoon subjects? Will they transition their focus and training for tomorrow? How will education change, how will assessment change, and how will educational structure change?
We will speculate this in Part 3 of this series.
If you missed part 1, here is the link : https://icenet.blog/2025/04/29/microwaves-chefs-and-restaurants/
References/Further Reading
Launching the Artificial Intelligence Playbook for the UK Government. https://www.gov.uk/government/publications/ai-playbook-for-the-uk-governmentcare.
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