In the recent systematic review published in The Lancet Digital Health, researchers delved into the perceptions and concerns surrounding machine learning (ML)-based risk prediction models in healthcare. The study aimed to analyze healthcare professionals’ and patients’ perceptions of these models and identify any gaps in knowledge or discrepancies in views between the two groups.
Upon thorough examination of 41 articles, the review discovered that overall perceptions of ML-based risk prediction models were positive. However, the findings also revealed areas of concern and reservations among healthcare professionals and patients.
One of the main gaps in knowledge identified in the study was the need for further research to determine optimal methods for model explanation and alerts. Ensuring transparency and explainability of ML models is crucial to gaining trust and acceptance from both healthcare professionals and patients. Developing robust techniques for explaining model predictions can help alleviate concerns and increase the adoption of ML-based risk prediction models in healthcare.
Additionally, the review highlighted the importance of understanding patients’ perceptions of ML-based predictive models as they play a crucial role in accepting and utilizing these models in their healthcare decisions. Therefore, it is essential to also consider their perspectives, preferences, and understanding of the technology to ensure effective communication and successful implementation.
The study pointed out that as the field of personalized medicine continues to advance, there is an increasing demand for complex risk prediction tools. ML methods, such as polygenic risk scores, are being explored to provide more accurate assessments of individual risk profiles. However, the current use of these tools is limited within healthcare settings, further emphasizing the need for research and validation to pave the way for wider adoption.
In summary, while ML-based risk prediction models show promise in healthcare, the systematic review revealed the importance of addressing concerns and reservations among healthcare professionals and patients. By bridging knowledge gaps, developing transparent explanations, and understanding patient perceptions, the field can work towards maximizing the potential benefits of ML in risk prediction and personalized medicine. Further research and collaboration between researchers, healthcare professionals, and patients are key to achieving this goal.
