In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient.

Intelligent agents are showing increasing promise for clinical decision-making in a variety of healthcare settings. While a substantial body of work has contributed to the best strategies to convey these agents’ decisions to clinicians, few have considered the impact of personalizing and customizing these communications on the clinicians’ performance and receptiveness. This raises the question of how intelligent agents should adapt their tone in accordance with their target audience. We designed two approaches to communicate the decisions of an intelligent agent for breast cancer diagnosis with different tones: a suggestive (non-assertive) tone and an imposing (assertive) one. We used an intelligent agent to inform about: (1) number of detected findings; (2) cancer severity on each breast and per medical imaging modality; (3) visual scale representing severity estimates; (4) the sensitivity and specificity of the agent; and (5) clinical arguments of the patient, such as pathological co-variables. Our results demonstrate that assertiveness plays an important role in how this communication is perceived and its benefits. We show that personalizing assertiveness according to the professional experience of each clinician can reduce medical errors and increase satisfaction, bringing a novel perspective to the design of adaptive communication between intelligent agents and clinicians.

Statistical shape models have been extensively used in a wide range of applications due to their effectiveness in providing prior shape information for object segmentation problems. The most popular method is the active shape model (ASM). However, accurately fitting the shape model to an object boundary under a cluttered environment is a challenging task. Under such assumptions, the model is often attracted toward invalid observations (outliers), leading to meaningless estimates of the object boundary. In this paper, we propose a novel algorithm that improves the robustness of ASM in the presence of outliers. The proposed framework assumes that both type of observations (valid observations and outliers) are detected in the image. A new strategy is devised for treating the data in different ways, depending on the observations being considered as valid or invalid. The proposed algorithm assigns a different weight to each observation. The shape parameters are recursively updated using the expectation-maximization method, allowing a correct and robust fit of the shape model to the object boundary in the image. Two estimation criteria are considered: 1) the maximum likelihood criterion and 2) the maximum a posteriori criterion that use priors for the unknown parameters. The methods are tested with synthetic and real images, comprising medical images of the heart and image sequences of the lips. The results are promising and show that this approach is robust in the presence of outliers, leading to a significant improvement over the standard ASM and other state-of-the-art methods.