EXPLAINABLE AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEMS IN PRECISION ONCOLOGY: INTERPRETABLE MODELS FOR MULTIMODAL CANCER CARE
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866902031373959168 |
|---|---|
| author | Ohmini Krishnamurthy Rajendran |
| author_facet | Ohmini Krishnamurthy Rajendran |
| contents | <p class="MsoNormal"><strong><em><span>A potential advancement in precision oncology is AI in clinical decision support systems (CDSSs). In order to assist physicians in making more individualized treatment recommendations, AI can examine vast and complex data from electronic health records, radiology, histology, genetics, and other fields. Beyond biomarker-based patient classification, finding novel patterns and connections within and across various data sources is necessary. Risk is measured, diagnostic accuracy is improved, and patient outcomes, such as survival, treatment response, and recurrence, are predicted by AI applications in this field. Clinical Decision Support Systems (CDSS) with AI support may be able to synthesize data that cannot be interpreted by humans, allowing for the creation of brand-new biomarkers. The diagnosis of pulmonary nodules on a CT scan is consistent with AI models. Security of data, representation, and the ability to explain AI-based forecasts are still major obstacles. Up until 2025, over 139 peer-reviewed papers will cover a substantial patient database of solid and hemorrhagic cancers. AUC values of 0.82–0.96 for multimodal AI models versus 0.65–0.78 for single-modality or rule-based clinical models indicate that multimodal AI models outperform biomarker-based stratification when it comes to predictive performance. The technological viability, validation levels, implementation environment, and user group all influence the significance of explainability in the CDSS. For AI to be realistically and widely used in multimodal cancer therapy and for personalized medicine to advance, these obstacles must be overcome.</span></em></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20328194 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | EXPLAINABLE AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEMS IN PRECISION ONCOLOGY: INTERPRETABLE MODELS FOR MULTIMODAL CANCER CARE Ohmini Krishnamurthy Rajendran Artificial Intelligence, Clinical Decision Support, Precision Oncology, Outcome Prediction, Data Security, Personalized Medicine. <p class="MsoNormal"><strong><em><span>A potential advancement in precision oncology is AI in clinical decision support systems (CDSSs). In order to assist physicians in making more individualized treatment recommendations, AI can examine vast and complex data from electronic health records, radiology, histology, genetics, and other fields. Beyond biomarker-based patient classification, finding novel patterns and connections within and across various data sources is necessary. Risk is measured, diagnostic accuracy is improved, and patient outcomes, such as survival, treatment response, and recurrence, are predicted by AI applications in this field. Clinical Decision Support Systems (CDSS) with AI support may be able to synthesize data that cannot be interpreted by humans, allowing for the creation of brand-new biomarkers. The diagnosis of pulmonary nodules on a CT scan is consistent with AI models. Security of data, representation, and the ability to explain AI-based forecasts are still major obstacles. Up until 2025, over 139 peer-reviewed papers will cover a substantial patient database of solid and hemorrhagic cancers. AUC values of 0.82–0.96 for multimodal AI models versus 0.65–0.78 for single-modality or rule-based clinical models indicate that multimodal AI models outperform biomarker-based stratification when it comes to predictive performance. The technological viability, validation levels, implementation environment, and user group all influence the significance of explainability in the CDSS. For AI to be realistically and widely used in multimodal cancer therapy and for personalized medicine to advance, these obstacles must be overcome.</span></em></strong></p> |
| title | EXPLAINABLE AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEMS IN PRECISION ONCOLOGY: INTERPRETABLE MODELS FOR MULTIMODAL CANCER CARE |
| topic | Artificial Intelligence, Clinical Decision Support, Precision Oncology, Outcome Prediction, Data Security, Personalized Medicine. |
| url | https://doi.org/10.5281/zenodo.20328194 |