Introduction
- AI Definition: Refers to machines designed to mimic human intelligence.
- Tasks AI can perform: From basic ones like reading to more complex ones like self-driving cars and generating human-like text (e.g., ChatGPT).
- Focus in Healthcare: AI could greatly improve patient care but needs careful implementation and oversight.
AI in Health Care: Key Areas
- Not a New Concept: Early medical AI models like MYCIN (1970s) were used for diagnosing infections.
- Recent Advancements: AI is now used in areas like:
- Medical Imaging: Helps interpret X-rays, MRI scans, etc.
- Telemedicine: Allows remote patient consultations.
- Genomics: Aids in understanding genetic disorders.
- Surgery: Robotic assistance in complex procedures.
Potential areas of AI in medicine | ||
Clinician-facing | Patient-facing | Non-clinical |
Diagnostic programs e.g. CTG interpretation | Symptom tracking e.g. in chronic disease control | Administrative tasks e.g. appointment scheduling |
Treatment optimisation e.g. antibiotic selection | Pain management e.g. in neuropathic pain | Medical education e.g. virtual reality training |
Image interpretation e.g. X-ray screening | Medical chatbots e.g. patient triage apps | Systematic review synthesis e.g. abstract screening |
Robotic-assisted surgery | Telemedicine | Drug discovery |
Non-Clinical Roles of AI in Medicine
- Medical Education: Virtual training programs for students.
- Research: Speeds up drug discovery and testing.
- Patient Access to Health Data: Tools like health apps and wearables let patients track their own health, promoting personalized care.
How AI Works in Healthcare
- Pattern Recognition: AI identifies patterns in large datasets to support decisions.
- Supervised Learning: AI learns from data labeled by humans (e.g., a dataset of images labeled as "cancerous" or "healthy").
- Real-World Example: AI can analyze cardiotocography (CTG) readings in obstetrics to detect fetal distress.
AI in Obstetrics and Gynecology
