From tedious to targeted: Optimizing oral cancer research with Consensus AI
Barriers which include subjective biases, overabundance of data, and budget limitations impede oral cancer research. Conventional techniques consume an extensive amount of time, are biased, and have limitations. Consensus AI, on the other hand, presents a viable alternative by effectively sorting th...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Oral Oncology Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024002292 |
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| Summary: | Barriers which include subjective biases, overabundance of data, and budget limitations impede oral cancer research. Conventional techniques consume an extensive amount of time, are biased, and have limitations. Consensus AI, on the other hand, presents a viable alternative by effectively sorting through enormous datasets by utilizing several AI algorithms. Consensus AI reduces biases, increases efficiency, and improves article selection dependability by merging a variety of AI models. Research on oral cancer may be able to make more accurate predictions and get deeper insights because to its capacity to combine different data sources and apply ensemble learning. The editorial explore the role of Consensus AI in method optimization. |
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| ISSN: | 2772-9060 |