| 000 | 01333cam a2200205 4500500 | ||
|---|---|---|---|
| 005 | 20250123102023.0 | ||
| 041 | _afre | ||
| 042 | _adc | ||
| 100 | 1 | 0 |
_aBajema, Ingeborg M. _eauthor |
| 245 | 0 | 0 | _aMachine learning in medicine: Medical droids, tricorders, and a computer named Hal 9000 |
| 260 | _c2021. | ||
| 500 | _a96 | ||
| 520 | _aThe usage of artificial intelligence to evaluate histological images was recently explored in many different areas of pathology. Studies focusing on nephropathology demonstrated that algorithms could be trained to identify various structures of the kidney, like glomeruli and interstitium, as well as performing a classification task just as good as highly experienced pathologists. It is conceivable that further development of digitalized pathology in combination with all opportunities that artificial intelligence and machine learning have to offer, will rapidly change the work of the clinical pathologist in a substantial way. | ||
| 690 | _aMachine learning | ||
| 690 | _aKidney biopsy | ||
| 690 | _aNephropathology | ||
| 690 | _aArtificial intelligence | ||
| 786 | 0 | _nNéphrologie & Thérapeutique | Volume 17 | N° Supp 1 | 2021-01-15 | p. 51-53 | 1769-7255 | |
| 856 | 4 | 1 | _uhttps://shs.cairn.info/revue-nephrologie-et-therapeutique-2021-HS1-page-51?lang=en&redirect-ssocas=7080 |
| 999 |
_c759940 _d759940 |
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