10clinimp.Rmd

# Clinical Implication

```{r setup10, include=FALSE}
require(Publish)
```

## ML in clinical settings

We could have the following uses of ML methods in the clinical settings:

- **prescreen** patients to identify high-risk patient pool.
  - warn patients about imminent risk
  - helps manage clinical workload
- help **diagnose** a disease better with high accuracy
  - could be based on radiology or pathology images
  - could prevent mis-diagnose by giving a second opinion
  - could indicate suspicious regions, assisting clinicians to focus on the most important considerations
- **Monitoring** vulnerable patients
  - monitoring devices (e.g., fall detection)
  - ethical, moral and transparency considerations

## Model updating

- **Updating** risk prediction model based on new data
  - could be automated given access to continuously collected data
- policy could change, requiring the update of the model