Human-in-the-Loop (HITL) Training is an AI training approach where humans actively participate in improving AI models by providing feedback, correcting errors, and handling edge cases. This iterative process ensures that AI systems learn from human expertise, refining their accuracy, adaptability, and decision-making capabilities.
How HITL Training Works:
AI makes a prediction – The model processes data and generates an output.
Human reviews & corrects – A human expert evaluates the result, correcting errors or providing additional insights.
Model updates & improves – The AI retrains on the corrected data, reducing future errors and improving accuracy.
Loop repeats as needed – The cycle continues until the AI reaches a desired performance level.
Human-in-the-loop (HITL) training is crucial because humans excel in areas where AI still struggles, especially when it comes to nuance, context, and decision-making under uncertainty. The collaboration between human expertise and AI efficiency is what allows AI models to do “impossibly awesome things.”
We don't think HITL (a very well understood concept in AI training) does justice for doctor expertise. Doctors aren't just any old human, they're humans that have spent ungodly amounts of time developing their expertise and wisdom. That's why we coined a new term, Doctor-in-the-Loop™.
With that in mind, here’s a breakdown of the core principles that dictate when and why humans outperform AI.
Ambiguity & Context Understanding
Why Humans Win: Humans can interpret ambiguous language, read between the lines, and understand subtle social and cultural cues that AI often misinterprets.
Example: A doctor reading a patient’s vague symptom description (“I just feel off”) can infer potential causes based on years of experience and contextual knowledge.
Ethical and Moral Reasoning
Why Humans Win: AI lacks moral reasoning and the ability to weigh ethical trade-offs. Humans understand fairness, justice, and the impact of decisions on people’s lives.
Example: A healthcare AI may flag a patient as low-priority based on algorithmic risk scores, but a doctor can override this when considering social determinants of health.
Novelty & Adaptation
Why Humans Win: AI is trained on past data and struggles with entirely new situations, whereas humans can generalize from prior experiences and adapt.
Example: A radiologist might notice an unusual pattern on an X-ray that is not in any existing AI training set and recognize it as a new disease presentation.
Creativity & Intuition
Why Humans Win: AI generates based on learned patterns, but it doesn’t truly create in the way humans do. Humans use intuition to make leaps in reasoning that AI can’t.
Example: A physician brainstorming an innovative treatment plan for a patient with a rare condition, combining off-label medication use and lifestyle changes in a way no AI model would suggest.
Explainability & Trust
Why Humans Win: AI models often function as “black boxes,” whereas humans can explain their reasoning in ways that build trust and accountability.
Example: A doctor can justify why they chose a particular treatment based on experience, patient history, and emerging research, rather than just probabilities.
Outlier Detection & Common Sense
Why Humans Win: AI models often struggle with edge cases and data outliers. Humans can recognize when something just “doesn’t look right.”
Example: An AI chatbot might misinterpret a patient’s sarcasm or an unusual medical history, but a human can immediately recognize and correct the misunderstanding.
Emotional Intelligence & Empathy
Why Humans Win: AI lacks true emotional intelligence. Humans can understand emotions, respond with empathy, and provide reassurance.
Example: A doctor delivering bad news does so with emotional sensitivity—something an AI assistant cannot replicate in a meaningful way.
Doctor-in-the-Loop™
Human-in-the-Loop model training improves AI by incorporating human expertise to review, refine, and correct the model’s performance, but it’s not enough when building tools for something as complex and high-stakes as healthcare. That’s why Automate Clinic created Doctor-in-the-Loop™, a groundbreaking approach that puts physicians directly at the center of AI training. Doctors bring unique knowledge and clinical intuition that no dataset alone can replicate. By involving them in the training process—validating data, correcting errors, and refining outputs—we ensure AI models reflect the nuanced realities of clinical practice.
This methodology is critically important because healthcare isn’t one-size-fits-all. Models trained without direct input from doctors risk misunderstanding context, misinterpreting complex medical language, or generating outputs that disrupt workflows rather than improve them. With Doctor-in-the-Loop™, we are training AI to think like a doctor, ensuring it delivers recommendations and tools that are practical, reliable, and clinically sound. This not only creates the highest-quality models imaginable but also ensures these models seamlessly integrate into care delivery, improving outcomes for doctors, patients, and the healthcare system as a whole.
Are you a doctor interested in the future of healthcare?
Curious to see how Automate Clinic can help your model accuracy?
Human-in-the-Loop (HITL) Training is an AI training approach where humans actively participate in improving AI models by providing feedback, correcting errors, and handling edge cases. This iterative process ensures that AI systems learn from human expertise, refining their accuracy, adaptability, and decision-making capabilities.
How HITL Training Works:
AI makes a prediction – The model processes data and generates an output.
Human reviews & corrects – A human expert evaluates the result, correcting errors or providing additional insights.
Model updates & improves – The AI retrains on the corrected data, reducing future errors and improving accuracy.
Loop repeats as needed – The cycle continues until the AI reaches a desired performance level.
Human-in-the-loop (HITL) training is crucial because humans excel in areas where AI still struggles, especially when it comes to nuance, context, and decision-making under uncertainty. The collaboration between human expertise and AI efficiency is what allows AI models to do “impossibly awesome things.”
We don't think HITL (a very well understood concept in AI training) does justice for doctor expertise. Doctors aren't just any old human, they're humans that have spent ungodly amounts of time developing their expertise and wisdom. That's why we coined a new term, Doctor-in-the-Loop™.
With that in mind, here’s a breakdown of the core principles that dictate when and why humans outperform AI.
Ambiguity & Context Understanding
Why Humans Win: Humans can interpret ambiguous language, read between the lines, and understand subtle social and cultural cues that AI often misinterprets.
Example: A doctor reading a patient’s vague symptom description (“I just feel off”) can infer potential causes based on years of experience and contextual knowledge.
Ethical and Moral Reasoning
Why Humans Win: AI lacks moral reasoning and the ability to weigh ethical trade-offs. Humans understand fairness, justice, and the impact of decisions on people’s lives.
Example: A healthcare AI may flag a patient as low-priority based on algorithmic risk scores, but a doctor can override this when considering social determinants of health.
Novelty & Adaptation
Why Humans Win: AI is trained on past data and struggles with entirely new situations, whereas humans can generalize from prior experiences and adapt.
Example: A radiologist might notice an unusual pattern on an X-ray that is not in any existing AI training set and recognize it as a new disease presentation.
Creativity & Intuition
Why Humans Win: AI generates based on learned patterns, but it doesn’t truly create in the way humans do. Humans use intuition to make leaps in reasoning that AI can’t.
Example: A physician brainstorming an innovative treatment plan for a patient with a rare condition, combining off-label medication use and lifestyle changes in a way no AI model would suggest.
Explainability & Trust
Why Humans Win: AI models often function as “black boxes,” whereas humans can explain their reasoning in ways that build trust and accountability.
Example: A doctor can justify why they chose a particular treatment based on experience, patient history, and emerging research, rather than just probabilities.
Outlier Detection & Common Sense
Why Humans Win: AI models often struggle with edge cases and data outliers. Humans can recognize when something just “doesn’t look right.”
Example: An AI chatbot might misinterpret a patient’s sarcasm or an unusual medical history, but a human can immediately recognize and correct the misunderstanding.
Emotional Intelligence & Empathy
Why Humans Win: AI lacks true emotional intelligence. Humans can understand emotions, respond with empathy, and provide reassurance.
Example: A doctor delivering bad news does so with emotional sensitivity—something an AI assistant cannot replicate in a meaningful way.
Doctor-in-the-Loop™
Human-in-the-Loop model training improves AI by incorporating human expertise to review, refine, and correct the model’s performance, but it’s not enough when building tools for something as complex and high-stakes as healthcare. That’s why Automate Clinic created Doctor-in-the-Loop™, a groundbreaking approach that puts physicians directly at the center of AI training. Doctors bring unique knowledge and clinical intuition that no dataset alone can replicate. By involving them in the training process—validating data, correcting errors, and refining outputs—we ensure AI models reflect the nuanced realities of clinical practice.
This methodology is critically important because healthcare isn’t one-size-fits-all. Models trained without direct input from doctors risk misunderstanding context, misinterpreting complex medical language, or generating outputs that disrupt workflows rather than improve them. With Doctor-in-the-Loop™, we are training AI to think like a doctor, ensuring it delivers recommendations and tools that are practical, reliable, and clinically sound. This not only creates the highest-quality models imaginable but also ensures these models seamlessly integrate into care delivery, improving outcomes for doctors, patients, and the healthcare system as a whole.
Are you a doctor interested in the future of healthcare?
Curious to see how Automate Clinic can help your model accuracy?
Human-in-the-Loop (HITL) Training is an AI training approach where humans actively participate in improving AI models by providing feedback, correcting errors, and handling edge cases. This iterative process ensures that AI systems learn from human expertise, refining their accuracy, adaptability, and decision-making capabilities.
How HITL Training Works:
AI makes a prediction – The model processes data and generates an output.
Human reviews & corrects – A human expert evaluates the result, correcting errors or providing additional insights.
Model updates & improves – The AI retrains on the corrected data, reducing future errors and improving accuracy.
Loop repeats as needed – The cycle continues until the AI reaches a desired performance level.
Human-in-the-loop (HITL) training is crucial because humans excel in areas where AI still struggles, especially when it comes to nuance, context, and decision-making under uncertainty. The collaboration between human expertise and AI efficiency is what allows AI models to do “impossibly awesome things.”
We don't think HITL (a very well understood concept in AI training) does justice for doctor expertise. Doctors aren't just any old human, they're humans that have spent ungodly amounts of time developing their expertise and wisdom. That's why we coined a new term, Doctor-in-the-Loop™.
With that in mind, here’s a breakdown of the core principles that dictate when and why humans outperform AI.
Ambiguity & Context Understanding
Why Humans Win: Humans can interpret ambiguous language, read between the lines, and understand subtle social and cultural cues that AI often misinterprets.
Example: A doctor reading a patient’s vague symptom description (“I just feel off”) can infer potential causes based on years of experience and contextual knowledge.
Ethical and Moral Reasoning
Why Humans Win: AI lacks moral reasoning and the ability to weigh ethical trade-offs. Humans understand fairness, justice, and the impact of decisions on people’s lives.
Example: A healthcare AI may flag a patient as low-priority based on algorithmic risk scores, but a doctor can override this when considering social determinants of health.
Novelty & Adaptation
Why Humans Win: AI is trained on past data and struggles with entirely new situations, whereas humans can generalize from prior experiences and adapt.
Example: A radiologist might notice an unusual pattern on an X-ray that is not in any existing AI training set and recognize it as a new disease presentation.
Creativity & Intuition
Why Humans Win: AI generates based on learned patterns, but it doesn’t truly create in the way humans do. Humans use intuition to make leaps in reasoning that AI can’t.
Example: A physician brainstorming an innovative treatment plan for a patient with a rare condition, combining off-label medication use and lifestyle changes in a way no AI model would suggest.
Explainability & Trust
Why Humans Win: AI models often function as “black boxes,” whereas humans can explain their reasoning in ways that build trust and accountability.
Example: A doctor can justify why they chose a particular treatment based on experience, patient history, and emerging research, rather than just probabilities.
Outlier Detection & Common Sense
Why Humans Win: AI models often struggle with edge cases and data outliers. Humans can recognize when something just “doesn’t look right.”
Example: An AI chatbot might misinterpret a patient’s sarcasm or an unusual medical history, but a human can immediately recognize and correct the misunderstanding.
Emotional Intelligence & Empathy
Why Humans Win: AI lacks true emotional intelligence. Humans can understand emotions, respond with empathy, and provide reassurance.
Example: A doctor delivering bad news does so with emotional sensitivity—something an AI assistant cannot replicate in a meaningful way.
Doctor-in-the-Loop™
Human-in-the-Loop model training improves AI by incorporating human expertise to review, refine, and correct the model’s performance, but it’s not enough when building tools for something as complex and high-stakes as healthcare. That’s why Automate Clinic created Doctor-in-the-Loop™, a groundbreaking approach that puts physicians directly at the center of AI training. Doctors bring unique knowledge and clinical intuition that no dataset alone can replicate. By involving them in the training process—validating data, correcting errors, and refining outputs—we ensure AI models reflect the nuanced realities of clinical practice.
This methodology is critically important because healthcare isn’t one-size-fits-all. Models trained without direct input from doctors risk misunderstanding context, misinterpreting complex medical language, or generating outputs that disrupt workflows rather than improve them. With Doctor-in-the-Loop™, we are training AI to think like a doctor, ensuring it delivers recommendations and tools that are practical, reliable, and clinically sound. This not only creates the highest-quality models imaginable but also ensures these models seamlessly integrate into care delivery, improving outcomes for doctors, patients, and the healthcare system as a whole.
Are you a doctor interested in the future of healthcare?
Curious to see how Automate Clinic can help your model accuracy?