reviews: 
Comments: This paper proposes an end-to-end system for the classification of musculoskeletal radiograph images. The system is formulated in the context of multi-modal learning, and comprises an ensemble of methods that are combined by taking into account the uncertainty estimates that they produce.
Strengths: To name one, the broad topic that this paper covers, i.e. uncertainty estimation in medical imaging, is an important one.
Weaknesses: * The whole writing in the paper is very vague and superficial. Just reading the abstract+intro is a clear example of this. Reading the abstract, it seems like the main contributions are 1) the URF technique, 2) an image acquisition model (which seems a bit disconnected from the URF thing, but one expects to find the connection in the intro), 3) the GUE technique. However, nothing is said about the last two points in the intro, completely missing context for the contribution in this sense. Indeed, the first four paragraphs in the intro are very general sentences that do not provide any concrete idea. They just revolve around the idea of "uncertainty is important in medical imaging". Also, the focus in the title and abstract is in "healthcare" (very general); however, in the intro the focus turns way more specific and only "musculoskeletal problems" are mentioned. 

* No experiments are provided in the main text. As indicated in the next point, the theoretical/methodological contribution is very minor. Therefore, the interest of the paper would be in an extensive empirical validation of the proposed idea. However, there are no experiments in the main text. There are experiments in the appendix, but pretty disconnected from the main text and lacking a clear experimental setting. 

* The methodological contribution, URF, seems to be described in the fourth paragraph of Section 2.1. It is not easy to understand, because the formulation of the problem is again very vague and poorly described (first paragraph of section 2.1). It is also difficult to evaluate the novelty of this idea, because no related work is discussed in detail. They just construct a baseline fusion procedure called Vanilla Fusion (paragraphs 2 and 3 of section 2.1), and propose their URF building on this VF. 

* According to section 2.1, second paragraph, "Figure 1 shows a graphic illustration of the VF, URF, and URFw methods". However, after reading the descriptions of these three methods in section 2.1, I still do not see how Figure 1 covers those three approaches. Indeed, nothing about these methods is said in the caption of the figure or in the figure itself. 

* Some sentences lack coherence. As a clear example, see section 2.1, third paragraph, first sentence: 

"In Vanilla Fusion (VF), the weighting of the loss function is adjusted during training while iteratively boosting among the base learners using loss data, such as Mean Squared Error (MSE) for regression, Kullback-Leibler (KL) divergence loss the discrepancy between the actual distribution of class probabilities and the expected class probabilities and IoU or Jaccard Loss evaluates the overlap between
anticipated and ground truth masks and is frequently used in semantic segmentation problems."
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 1 poor