Table 1. Gioia coding structure Example...

Table 1.

Gioia coding structure

Example quotes	First-order concepts	Second-order themes	Aggregate dimensions
The AI we currently use is almost just a really highly advanced calculator with enormous performance	AI is primarily used for narrow, task-specific applications	Narrow and task-specific AI applications	Sustained clinical competence amidst current AI integration
AI only counts the cells and performs the quantification	AI performs computational tasks without analytical reasoning
For us, AI is more of a sophisticated tool, not something that completely changes our core skills	AI is a supportive tool that complements clinical expertise	Supportive tool rather than a disruptive force
However, I do not see any major threats to our competence	AI poses no major threats to current competence
It is during the multidisciplinary discussions with oncologists and radiologists that the results are presented and statements … The oncologist then looks at the results, together with their own algorithms, and determines which treatment to use	Multidisciplinary decision-making process
Just as a calculator hasn’t diminished math skills, AI is enhancing clinical work without reducing core skills	No observed deskilling with current narrow AI applications	Perceived absence of deskilling among current practitioners
It’s like when calculators were introduced, and people thought it would make us worse at math. We haven’t lost math skills by using calculators; we’ve just upgraded the level	Current tools enhance clinical work without reducing skills
Despite automation, no one has lost competence yet; it’s just helped us be more precise	Automation increases precision without reducing competence
We still belong to a generation that is fully trained in making independent assessments, and I believe that is crucial	Confidence in current clinicians’ ability to make independent decisions
Quantity training … simply looking at a lot, a lot of images that build up some kind of knowledge bank in the head … how can it be ensured? If double-checking disappears, for example	Risk of losing foundational knowledge-building through hands-on image review	Declining opportunities for foundational skill development	Future risk of deskilling
With future generations, there is a risk they might not develop foundational skills	Risk of skill underdevelopment
What happens to those who start performing surgeries with robots without ever learning the classical methods?	Concern for erosion of fundamental surgical skills due to reliance on robotics
Double-reading with AI [instead of two humans] risks diminishing training for junior radiologists who benefit from extensive case review with senior professionals	Junior radiologists risk reduced training opportunities with AI
All tools have the potential to erode competence	The inherent risk of AI-tools to deskill	Ever-present risk of deskilling
It’s important to keep the human aspect … if we rely too much on AI, we might lose the human creativity and curiosity that often leads to important discoveries	Risk of losing human creativity and curiosity due to over-reliance on AI	Erosion of human-driven innovation and foundational skills in AI-reliant environments
If AI says one thing and I think differently, how do I handle that? Or worse, I might defer to AI’s judgment, assuming it’s correct, even when I would have reached a different decision on my own	Risk of deferring to AI judgment over personal clinical reasoning	Automation bias among less experienced clinicians
When you’re inexperienced, it’s easy to lean on the AI’s assessment if it differs from your own. There’s a risk this could negatively affect your own judgment	Inexperience increases reliance on AI, risking diminished independent judgment
What will be the long-term change from this? That we become dependent on technology, and that much of the competence resides within IT rather than in healthcare?	Long-term risks of dependency on technology over clinical skills	Expanding capabilities of AI
My perception, intuitive and speculative, is that we will move towards a general intelligence that will essentially handle 80% of jobs	Potential for general AI to replace a significant portion of tasks in the job market
I don’t think it [deskilling] is a significant issue today, but it has the potential to quickly develop into a major problem. The reason is that the implementation of these models will occur exponentially, and the models themselves will become increasingly complex at the same pace	Exponential implementation and increasing complexity in AI models leading to deskilling
As a result, we risk losing a crucial dimension: the transition from junior to senior levels within various professions
If double-reading disappears because of AI, we need to establish new processes to ensure residents still get the experience they need. Maybe involving a resident, a radiologist, and AI together	New processes needed to ensure experience	Redesign training framework to sustain competence	Need for adapted education and training
It’s crucial that training adapts, ensuring future radiologists don’t rely solely on AI, as that could affect their skills	Adapting training to balance reliance on AI and skill retention
There needs to be regular exposure so that competence doesn’t erode	Find a way to ensure continuous exposure
AI can be a fantastic tool for enhancing pathologists’ training. It gives us a chance to get faster feedback on our assessments, which can be very helpful in the learning process	AI as a tool for hands-on learning and feedback	AI as a tool for learning	AI for upskilling
Digital pathology and AI are a major asset for training in pathology	AI as a training asset
When AI and human experts combine, the quality improves significantly	Collaboration between AI and humans improves diagnostic quality	Enhanced clinical outcomes through combined human–AI capability
I believe AI enhances radiological competence	AI enhance radiological competence
It would be extremely valuable to have two assessors for each individual cancer case, given how critical the diagnostics we are working with are	AI integration can enable double-review processes in diagnostics
The important thing here is that AI will not replace pathologists, but we will become much better pathologists by using AI	AI will enhance professional competence rather than replace pathologists
If we can develop an AI that eliminates many of the routine examinations, we will save a significant amount of resources. Those resources can then be allocated to more complex cases	Resource reallocation from routine tasks to complex cases with AI
We are in co-evolution with AI	Development in collaboration with AI
AI gives us more capacity, allowing us to focus on complex tasks and patient interaction	AI integration enhances capacity for complex tasks and interaction
I specifically sought out a workplace with advanced digital and AI use – it’s one of the important factors	Workplace appeal linked to AI use	AI integration contributes to professional satisfaction and meaningful work
We have students who have only worked with digital pathology, and they are very satisfied and knowledgeable	Students trained with AI are satisfied and knowledgeable

Example quotes	First-order concepts	Second-order themes	Aggregate dimensions
The AI we currently use is almost just a really highly advanced calculator with enormous performance	AI is primarily used for narrow, task-specific applications	Narrow and task-specific AI applications	Sustained clinical competence amidst current AI integration
AI only counts the cells and performs the quantification	AI performs computational tasks without analytical reasoning
For us, AI is more of a sophisticated tool, not something that completely changes our core skills	AI is a supportive tool that complements clinical expertise	Supportive tool rather than a disruptive force
However, I do not see any major threats to our competence	AI poses no major threats to current competence
It is during the multidisciplinary discussions with oncologists and radiologists that the results are presented and statements … The oncologist then looks at the results, together with their own algorithms, and determines which treatment to use	Multidisciplinary decision-making process
Just as a calculator hasn’t diminished math skills, AI is enhancing clinical work without reducing core skills	No observed deskilling with current narrow AI applications	Perceived absence of deskilling among current practitioners
It’s like when calculators were introduced, and people thought it would make us worse at math. We haven’t lost math skills by using calculators; we’ve just upgraded the level	Current tools enhance clinical work without reducing skills
Despite automation, no one has lost competence yet; it’s just helped us be more precise	Automation increases precision without reducing competence
We still belong to a generation that is fully trained in making independent assessments, and I believe that is crucial	Confidence in current clinicians’ ability to make independent decisions
Quantity training … simply looking at a lot, a lot of images that build up some kind of knowledge bank in the head … how can it be ensured? If double-checking disappears, for example	Risk of losing foundational knowledge-building through hands-on image review	Declining opportunities for foundational skill development	Future risk of deskilling
With future generations, there is a risk they might not develop foundational skills	Risk of skill underdevelopment
What happens to those who start performing surgeries with robots without ever learning the classical methods?	Concern for erosion of fundamental surgical skills due to reliance on robotics
Double-reading with AI [instead of two humans] risks diminishing training for junior radiologists who benefit from extensive case review with senior professionals	Junior radiologists risk reduced training opportunities with AI
All tools have the potential to erode competence	The inherent risk of AI-tools to deskill	Ever-present risk of deskilling
It’s important to keep the human aspect … if we rely too much on AI, we might lose the human creativity and curiosity that often leads to important discoveries	Risk of losing human creativity and curiosity due to over-reliance on AI	Erosion of human-driven innovation and foundational skills in AI-reliant environments
If AI says one thing and I think differently, how do I handle that? Or worse, I might defer to AI’s judgment, assuming it’s correct, even when I would have reached a different decision on my own	Risk of deferring to AI judgment over personal clinical reasoning	Automation bias among less experienced clinicians
When you’re inexperienced, it’s easy to lean on the AI’s assessment if it differs from your own. There’s a risk this could negatively affect your own judgment	Inexperience increases reliance on AI, risking diminished independent judgment
What will be the long-term change from this? That we become dependent on technology, and that much of the competence resides within IT rather than in healthcare?	Long-term risks of dependency on technology over clinical skills	Expanding capabilities of AI
My perception, intuitive and speculative, is that we will move towards a general intelligence that will essentially handle 80% of jobs	Potential for general AI to replace a significant portion of tasks in the job market
I don’t think it [deskilling] is a significant issue today, but it has the potential to quickly develop into a major problem. The reason is that the implementation of these models will occur exponentially, and the models themselves will become increasingly complex at the same pace	Exponential implementation and increasing complexity in AI models leading to deskilling
As a result, we risk losing a crucial dimension: the transition from junior to senior levels within various professions
If double-reading disappears because of AI, we need to establish new processes to ensure residents still get the experience they need. Maybe involving a resident, a radiologist, and AI together	New processes needed to ensure experience	Redesign training framework to sustain competence	Need for adapted education and training
It’s crucial that training adapts, ensuring future radiologists don’t rely solely on AI, as that could affect their skills	Adapting training to balance reliance on AI and skill retention
There needs to be regular exposure so that competence doesn’t erode	Find a way to ensure continuous exposure
AI can be a fantastic tool for enhancing pathologists’ training. It gives us a chance to get faster feedback on our assessments, which can be very helpful in the learning process	AI as a tool for hands-on learning and feedback	AI as a tool for learning	AI for upskilling
Digital pathology and AI are a major asset for training in pathology	AI as a training asset
When AI and human experts combine, the quality improves significantly	Collaboration between AI and humans improves diagnostic quality	Enhanced clinical outcomes through combined human–AI capability
I believe AI enhances radiological competence	AI enhance radiological competence
It would be extremely valuable to have two assessors for each individual cancer case, given how critical the diagnostics we are working with are	AI integration can enable double-review processes in diagnostics
The important thing here is that AI will not replace pathologists, but we will become much better pathologists by using AI	AI will enhance professional competence rather than replace pathologists
If we can develop an AI that eliminates many of the routine examinations, we will save a significant amount of resources. Those resources can then be allocated to more complex cases	Resource reallocation from routine tasks to complex cases with AI
We are in co-evolution with AI	Development in collaboration with AI
AI gives us more capacity, allowing us to focus on complex tasks and patient interaction	AI integration enhances capacity for complex tasks and interaction
I specifically sought out a workplace with advanced digital and AI use – it’s one of the important factors	Workplace appeal linked to AI use	AI integration contributes to professional satisfaction and meaningful work
We have students who have only worked with digital pathology, and they are very satisfied and knowledgeable	Students trained with AI are satisfied and knowledgeable

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