Artificial Intelligence in Health
GPTKB entity
Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:research
|
| gptkbp:benefits |
Increased efficiency
Cost reduction Improved accuracy |
| gptkbp:challenges |
Integration with existing systems
Data privacy concerns Regulatory hurdles Bias in algorithms |
| gptkbp:developed_by |
Healthcare professionals
Data scientists Software engineers |
| gptkbp:has_applications_in |
Medical imaging
Drug discovery Personalized medicine Disease diagnosis Patient monitoring Predictive analytics Treatment recommendations Robotic surgery Virtual health assistants Clinical trial research |
| gptkbp:has_impact_on |
Healthcare accessibility
Patient outcomes Healthcare costs |
| https://www.w3.org/2000/01/rdf-schema#label |
Artificial Intelligence in Health
|
| gptkbp:is_challenged_by |
Ethical dilemmas
Technological limitations Public skepticism |
| gptkbp:is_evaluated_by |
Clinical trials
Peer-reviewed studies |
| gptkbp:is_monitored_by |
Healthcare organizations
Regulatory agencies Ethics committees |
| gptkbp:is_promoted_by |
gptkb:Workshops
gptkb:Publications Conferences |
| gptkbp:is_related_to |
Big data
Machine learning Natural language processing Computer vision |
| gptkbp:is_supported_by |
Government funding
Private investment Academic research |
| gptkbp:is_trained_in |
Clinical guidelines
Patient records Medical data |
| gptkbp:is_used_by |
Research institutions
Hospitals Pharmaceutical companies |
| gptkbp:bfsParent |
gptkb:National_Institutes_of_Health
|
| gptkbp:bfsLayer |
3
|