A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Generative artificial intelligence (AI) shows potential in medical diagnostics, matching overall physician accuracy but not expert levels. Further research is needed to understand AI
Area Of Science
- Medical Informatics
- Artificial Intelligence in Medicine
- Diagnostic Performance Evaluation
Background
- Generative artificial intelligence (AI) shows promise for medical diagnostics.
- Comprehensive evaluation of AI diagnostic performance against physicians is limited.
- Understanding AI's capabilities and limitations is crucial for healthcare integration.
Purpose Of The Study
- To systematically review and meta-analyze studies on generative AI diagnostic performance.
- To compare the diagnostic accuracy of AI models with physicians (overall, expert, and non-expert).
- To assess the current state and potential of generative AI in medical diagnostics.
Main Methods
- Systematic review and meta-analysis of studies published between June 2018 and June 2024.
- Inclusion of studies validating generative AI models for diagnostic tasks.
- Statistical analysis to compare AI performance with physician performance.
Main Results
- Analysis of 83 studies revealed an overall AI diagnostic accuracy of 52.1%.
- No significant performance difference was found between AI models and physicians overall (p=0.10) or non-expert physicians (p=0.93).
- AI models performed significantly worse than expert physicians (p=0.007), though some models slightly outperformed non-experts.
Conclusions
- Generative AI exhibits promising diagnostic capabilities, with accuracy varying by model.
- Current AI models have not yet reached expert physician reliability levels.
- AI has potential to enhance healthcare delivery and medical education when limitations are understood.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...