What is Clinical AI? Applications of Artificial Intelligence in Patient Care
Introduction
In our first post, we defined Medical AI as the umbrella term for all artificial intelligence applications in healthcare. Within this broad field, Clinical AI refers specifically to AI tools designed to support direct patient care—diagnosis, prognosis, and treatment decision-making. Understanding Clinical AI is essential for clinicians because these systems directly influence patient outcomes.
Defining Clinical AI
Clinical AI includes machine learning models, natural language processing systems, and decision-support algorithms that interact with patient data to guide care. Unlike Administrative AI, which optimizes hospital operations, Clinical AI is patient-facing: its predictions or recommendations have immediate clinical implications.
Domains of Clinical AI
- Diagnostics. AI-assisted radiology, pathology, dermatology, and cardiology image interpretation.
- Prognostics. Risk prediction for ICU outcomes, disease progression, or hospital readmission.
- Therapeutics. Treatment selection, drug dosing optimization, robotic surgery guidance.
- Clinical operations at the bedside. Triage systems, sepsis alerts, patient deterioration warnings.
Clinical AI is one of the three core domains of Medical AI, alongside Administrative AI and Research AI.
Why Clinical AI Matters
- Direct patient impact. Clinical AI tools influence diagnoses, prognoses, and treatments.
- Efficiency & safety. Well-integrated systems reduce errors and enhance workflows.
- Professional responsibility. Clinicians must understand these tools to maintain oversight, especially under regulations like the EU AI Act.
Next in the Curriculum
The next post explores AI Literacy in Healthcare, outlining the competencies clinicians need to use AI responsibly and effectively. If you haven’t yet, read What is Medical AI? to understand the broader context.