A method to support clinical decision by characterizing the interpretability of images acquired in sequence through a video medical device, wherein the method comprises: • storing sequential images in a buffer; • for each image in the buffer, automatically determining at least one output of a first algorithm, said at least one output being based on at least one image quantitative criterion, wherein a first output of the first algorithm is an output value among a set of discrete values, wherein said first algorithm computes discrepancy measurement between local image descriptors to associate at least one region from one image to a region of another image, wherein the first algorithm is an unsupervised clustering algorithm that provides as the first output a cluster associated with each image; • performing a temporal segmentation of the images into a set of video subsequences made of consecutive images of equal first output; • displaying a timeline indicating positions of images in the buffer and attaching an output of the first algorithm to the timeline; and • processing at least one of the video subsequences using a second algorithm to provide at least one second output, wherein the second algorithm is an algorithm from a set of algorithms consisting of: a supervised classification algorithm configured to associate a predicted diagnostic or pathological class and a prediction confidence level with said at least one of the video subsequences; a supervised regression algorithm configured to estimate a probability of said at least one of the video subsequences belonging to a given pathological class; a supervised characterization algorithm configured to estimate the visual ambiguity of said at least one of the video subsequences with respect to a set of diagnostic or pathological classes; and an unsupervised characterization algorithm configured to estimate the visual atypicity of said at least one of the video subsequences. • storing sequential images in a buffer; • for each image in the buffer, automatically determining at least one output of a first algorithm, said at least one output being based on at least one image quantitative criterion, wherein a first output of the first algorithm is an output value among a set of discrete values, wherein said first algorithm computes discrepancy measurement between local image descriptors to associate at least one region from one image to a region of another image, wherein the first algorithm is an unsupervised clustering algorithm that provides as the first output a cluster associated with each image; • performing a temporal segmentation of the images into a set of video subsequences made of consecutive images of equal first output; • displaying a timeline indicating positions of images in the buffer and attaching an output of the first algorithm to the timeline; and • processing at least one of the video subsequences using a second algorithm to provide at least one second output, wherein the second algorithm is an algorithm from a set of algorithms consisting of: a supervised classification algorithm configured to associate a predicted diagnostic or pathological class and a prediction confidence level with said at least one of the video subsequences; a supervised regression algorithm configured to estimate a probability of said at least one of the video subsequences belonging to a given pathological class; a supervised characterization algorithm configured to estimate the visual ambiguity of said at least one of the video subsequences with respect to a set of diagnostic or pathological classes; and an unsupervised characterization algorithm configured to estimate the visual atypicity of said at least one of the video subsequences. a supervised classification algorithm configured to associate a predicted diagnostic or pathological class and a prediction confidence level with said at least one of the video subsequences; a supervised regression algorithm configured to estimate a probability of said at least one of the video subsequences belonging to a given pathological class; a supervised characterization algorithm configured to estimate the visual ambiguity of said at least one of the video subsequences with respect to a set of diagnostic or pathological classes; and an unsupervised characterization algorithm configured to estimate the visual atypicity of said at least one of the video subsequences.