Paper case studies give you the answer up front. Real patients don't. Here is how interactive AI patient simulation compares with the ways physiotherapy students usually practise clinical reasoning.
| Clinical Training Lab | Paper case studies | Generic AI chat | |
|---|---|---|---|
| You ask the questions | Yes — free-text interview | No — history is pre-written | Partly — no case structure |
| Hidden findings revealed only if you ask | Yes | No | Rarely |
| Red-flag screening checked against your transcript | Yes | No | No |
| Structured score with feedback | 100-point rubric | Self-marked | Inconsistent |
| Physiotherapy-specific cases | 240+ cases across body regions | Varies by textbook | Not curated |
| Senior clinician diagnosis reveal | Yes | Answer key | No |
| Educator tools | Assignments and class results | Manual marking | None |
A written case lists the history, the tests and often the diagnosis. You read reasoning instead of doing it, so the hardest skill — knowing what to ask next — never gets practised.
You interview a patient who answers naturally and only reveals what you ask about. You choose tests, screen for red flags and commit to a working diagnosis. Feedback then shows what you covered and what you missed.
General chatbots don't hide case facts, don't keep a consistent patient, and don't grade against a physiotherapy rubric. Clinical Training Lab cases are structured, scored the same way every time, and built for physiotherapy.
Written cases remain useful for revision and group discussion. Many learners use both: read to learn patterns, then simulate to test whether they can find them on their own.