Who Else Is in the Doctor’s Room? The Expanding Role of AI in Health Care

The Changing Face of the Examination Room

Traditionally, a doctor’s visit meant an interaction between the patient, caregiver, and physician. But today, the exam room has expanded to accommodate more unseen players—programmers, software developers, engineers, ethicists, data scientists, policymakers, and clinical validation experts—who shape the artificial intelligence (AI) tools that are transforming health care. AI is revolutionizing how doctors diagnose diseases, recommend treatments, and manage patient care. But who ensures that AI in medicine is effective, ethical, and beneficial for everyone?

The AI Life Cycle in Health Care

AI in health care follows a structured cycle that ensures it is accurate, ethical, and safe. Here’s a breakdown of the key stages, aligned with the considerations of equity and bias prevention:

1. Conceptualization

• Identify the health care problem AI aims to solve.

• Assess historical biases in access and disease prevalence to avoid inequitable outcomes.

  
  

2. Design

• Define AI’s intended use, ensuring ethical and clinical constraints are addressed.

• Prevent biases in algorithm design that may disproportionately impact certain populations.

  
  

3. Development

• Gather and train AI on diverse, representative datasets.

• Mitigate historical disparities in access to care and avoid biased exclusion criteria.

• Validate the reference standard used to train AI, ensuring that clinical outcomes are unbiased.

  
  

4. Validation

• Ensure clinical trials and validation studies include diverse patient groups.

• Identify and address biases in clinician-based reference standards.

• Reduce human factor issues affecting workflow and AI implementation.

  
  

5. Access & Implementation

• Address affordability concerns and prevent digital divides from limiting AI adoption.

• Ensure AI processes align with real-world health care settings for seamless integration.

  
  

6. Monitoring & Equity Analysis

• Establish patient-centered metrics for ethical AI assessment.

• Measure equitable access, clinical sensitivity/specificity, and performance variations across different demographic groups.

  
  

Each phase requires a collaborative effort from experts across different fields to ensure AI systems work for all patients, not just select populations.

Who’s at the Table? The Key Players in AI for Health Care

1. Programmers and Software Developers

• Build the algorithms that allow AI to “think” and process data.

• Ensure AI learns in a way that avoids bias and misinformation.

• Work closely with health care professionals to create user-friendly and effective AI tools.

  
  

2. Engineers

• Design and refine AI-powered medical devices, from wearable monitors to imaging software.

• Ensure AI integrates seamlessly with existing health care infrastructure.

  
  

3. Data Scientists

• Analyze massive datasets to train AI models.

• Identify and mitigate biases in AI learning to ensure fairness and accuracy.

  
  

4. Health care Professionals

• Provide the medical expertise AI needs to function correctly.

• Help test AI for accuracy and fairness in diverse clinical settings.

• Train other health care workers to use AI tools effectively.

  
  

5. Community Members & Patients

• Share insights into patient needs and concerns.

• Ensure AI tools are accessible and do not exacerbate health disparities.

• Provide real-world feedback to improve AI applications.

  
  

6. Ethicists and Policy Makers

• Develop guidelines for ethical AI use in health care.

• Advocate for transparency and accountability in AI decision-making.

• Shape regulations to ensure AI serves the public good.

  
  

7. Clinical Validation Experts

• Conduct unbiased validation studies to confirm AI’s real-world accuracy.

• Ensure AI models reflect diverse patient populations and prevent overfitting to a specific group.

• Align AI outcomes with recognized medical standards.

The Future of AI in Health Care

AI holds the potential to revolutionize health care, but only if it is developed collaboratively. By bringing together programmers, engineers, medical professionals, policymakers, and patient communities, we can build AI tools that improve global health outcomes while ensuring fairness, transparency, and trust. The exam room is no longer just for doctors and patients—it’s a collaborative space where technology, ethics, and human expertise must come together. The future of AI in health care depends on the collective efforts of all stakeholders to ensure it serves humanity for the better.

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Suggested References & Further Reading

• Forbes: “AI In Healthcare: The Next Frontier” (link)

• Nature npj Digital Medicine: “Considerations For Addressing Bias In Artificial Intelligence For Health Equity” (link)

• The Guardian: "The Ex-Radiologist Bringing AI to Health care" (link).

• The Washington Post: “The AI revolution in health care” (link)