Florian Markowetz discusses the gap between academic research and clinical implementation of clinical prediction models, emphasizing that most published models are never used in practice. He proposes strategies for researchers to make their models more impactful for patients. Markowetz highlights that academic success does not equate to clinical success, as academic papers focus on novelty and citations, while clinical success is measured by the model's practical use and impact on patient care. Key observations include: 1. **Success in Academia vs. Clinic**: Academic success is often measured by publications and citations, while clinical success is measured by the model's use and impact on patient outcomes. 2. **Data Availability**: Successful models use data available in routine practice, not just academic collections. 3. **Linked Actions**: Models must be linked to specific actions, such as treatment recommendations, to be useful. 4. **Implementation Beyond Centers of Excellence**: Models need to be implemented outside of elite centers to have broader impact. 5. **Regulatory Considerations**: Early planning for regulatory compliance is crucial for clinical adoption. Markowetz suggests that researchers should include implementation plans in their papers and engage with clinical practitioners to ensure their models are useful. He also emphasizes the importance of systemic changes, such as prioritizing implementation in promotion criteria and supporting real-world implementation efforts. Finally, he provides a checklist for developing useful clinical prediction tools, including steps to ensure the model's practical use, regulatory path, and readiness in the medical environment.Florian Markowetz discusses the gap between academic research and clinical implementation of clinical prediction models, emphasizing that most published models are never used in practice. He proposes strategies for researchers to make their models more impactful for patients. Markowetz highlights that academic success does not equate to clinical success, as academic papers focus on novelty and citations, while clinical success is measured by the model's practical use and impact on patient care. Key observations include: 1. **Success in Academia vs. Clinic**: Academic success is often measured by publications and citations, while clinical success is measured by the model's use and impact on patient outcomes. 2. **Data Availability**: Successful models use data available in routine practice, not just academic collections. 3. **Linked Actions**: Models must be linked to specific actions, such as treatment recommendations, to be useful. 4. **Implementation Beyond Centers of Excellence**: Models need to be implemented outside of elite centers to have broader impact. 5. **Regulatory Considerations**: Early planning for regulatory compliance is crucial for clinical adoption. Markowetz suggests that researchers should include implementation plans in their papers and engage with clinical practitioners to ensure their models are useful. He also emphasizes the importance of systemic changes, such as prioritizing implementation in promotion criteria and supporting real-world implementation efforts. Finally, he provides a checklist for developing useful clinical prediction tools, including steps to ensure the model's practical use, regulatory path, and readiness in the medical environment.