## 4 Discussion  
The key findings from the development and analysis of ML-MEDIC are as follows:  
* User interaction-based tools that do not require clinicians and biomedical experts to learn coding will facilitate machine learning due to ease of use. * Initially learning a subset of methods minimizes the learning-curve, especially when more explainable methods are used, could facilitate the adoption of machine learning and could aid in clinician's understanding of when certain machine learning methods can be applied as well as introduce trade-offs between interpretability and performance. * Ability to access tools in secure internet access settings can eliminate challenges relating to reusability and access. * A tool such as ML-MEDIC may provide a great educational resource for introducing data science and machine learning to clinical researchers. In summary, all clinicians positively expressed the likelihood of using ML-MEDIC or similar pipelines and tools developed through resources such as Shiny in R or Django in Python to facilitate machine learning due to ease of use. They confirmed that a limited set of models, with references for other resources, were effective for minimizing the learning curve and facilitating decisions when initially learning how to implement machine learning methods. All clinicians appreciated the layout, tabular views, and charts when analyzing and comparing models and suggested that the reactive nature of the tool aided in collaboration and exploration. Many clinicians with private datasets appreciated the security and accessibility the Precision Medicine Platform provided and the use of high performance computing to improve compute time. Given certain requests for the ability to install ML-MEDIC on a personal computer or institutional servers, the code is available on GitHub ([[https://github.com/lmrstevens3/Clinical_Data_in_Shiny.git](https://github.com/lmrstevens3/Clinical_Data_in_Shiny.git)]([https://github.com/lmrstevens3/Clinical_Data_in_Shiny.git](https://github.com/lmrstevens3/Clinical_Data_in_Shiny.git))) and the learn page of the Precision Medicine Platform. Initial feedback on ML-MEDIC's functionality and usability from the domain experts involved in the case studies was positive. An expert from case study 1 explained that \"the ability to isolate meaningful features intuitively allows users to quickly prototype more advanced ML modelsusing ML-MEDIC. As a result, clinicians can bring to bear the full power of predictive analytics on their datasets with only a lay understanding of data mining algorithms.\" The cardiologist from case study 2 concluded that \"ML-MEDIC easily demonstrated machine learning models could identify known risk factors associated with clinical outcomes, but also identify novel potential risk factors for better prediction of complex disease risk.\" He specified that \"easy-to-use and interactive tools would most likely be received well from both bioinformatics, statistical, and clinical audiences if they could confirm known findings while shortening the time for model building and evaluation\". These preliminary results regarding the usability of ML-MEDIC and its ability to facilitate learning and implementing machine learning in clinical analyses suggest the generalizability of the tools to other clinical domains beyond cardiology. During this review, researchers in psychology and other clinical domains were consulted, and they affirmed that the tools could expand beyond cardiology disciplines. Furthermore, researchers suggested that this type of tool could be very beneficial in educational settings, such as for undergraduates first learning machine learning or clinicians and biomedical students learning data science. Currently, a variety of tools offer machine learning capabilities, yet the complexity and diverse functionality of these tools can come at the cost of overwhelming users with options and potentially hinder adoption of machine learning.