| 1. |
- Robinson, W. Douglas, et al.
(författare)
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Integrating concepts and technologies to advance the study of bird migration
- 2010
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Ingår i: Frontiers in Ecology and the Environment. - : Wiley. - 1540-9309 .- 1540-9295. ; 8:7, s. 354-361
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Forskningsöversikt (refereegranskat)abstract
- Recent technological innovation has opened new avenues in migration research - for instance, by allowing individual migratory animals to be followed over great distances and long periods of time, as well as by recording physiological information. Here, we focus on how technology - specifically applied to bird migration - has advanced our knowledge of migratory connectivity, and the behavior, demography, ecology, and physiology of migrants. Anticipating the invention of new and smaller tracking devices, in addition to the ways that technologies may be combined to measure and record the behavior of migratory animals, we also summarize major conceptual questions that can only be addressed once innovative, cutting-edge instrumentation becomes available.
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| 2. |
- Oettl, Felix C., et al.
(författare)
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A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
- 2024
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Ingår i: JOURNAL OF EXPERIMENTAL ORTHOPAEDICS. - 2197-1153. ; 11:3
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Forskningsöversikt (refereegranskat)abstract
- Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.
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| 3. |
- Oettl, Felix C., et al.
(författare)
-
A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
- 2024
-
Ingår i: Journal of Experimental Orthopaedics. - 2197-1153. ; 11:3
-
Forskningsöversikt (refereegranskat)abstract
- Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.
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