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\shorttitle{BreastScreening-AI}
\shortauthors{Calisto et~al.}
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\title{Introduction of Human-Centric AI Assistant to Aid Radiologists for Multimodal Breast Image Classification}

\author[1]{Francisco Maria Calisto}[orcid=0000-0001-8179-7872]
\ead{francisco.calisto@tecnico.ulisboa.pt}
%\ead[URL]{https://web.tecnico.ulisboa.pt/francisco.calisto}

\author[1]{Carlos Santiago}[orcid=0000-0002-4737-0020]
%\ead{carlos.santiago@tecnico.ulisboa.pt}
%\ead[URL]{https://welcome.isr.tecnico.ulisboa.pt/author/carlosjorgemariz}

\author[2]{Nuno Nunes}[orcid=0000-0002-2498-0643]
%\ead{nunojnunes@tecnico.ulisboa.pt}
%\ead[URL]{https://fenix.tecnico.ulisboa.pt/homepage/ist132720}

\author[1]{Jacinto C. Nascimento}[orcid=0000-0001-7468-5127]
%\ead{jan@isr.tecnico.ulisboa.pt}
%\ead[URL]{http://users.isr.tecnico.ulisboa.pt/~jan}

\address[1]{Institute for Systems and Robotics, Avenida Rovisco Pais 1, 1049-001, Lisbon, Portugal}
\address[2]{Interactive Technologies Institute, Caminho da Penteada, 9020-105, Funchal, Madeira, Portugal}

\begin{abstract}
In this research, we take an HCI perspective on the opportunities provided by {\it AI techniques} in medical imaging, focusing on workflow efficiency and quality, preventing errors and variability of diagnosis in Breast Cancer. Starting from a holistic understanding of the clinical context, we developed {\it BreastScreening} to support {\it Multimodality} and integrate {\it AI techniques} (using a deep neural network to support automatic and reliable classification) in the medical diagnosis workflow.
This was assessed by using a significant number of clinical settings and radiologists.
Here we present:
i) user study findings of 45 physicians comprising nine clinical institutions;
ii) list of design recommendations for visualization to support breast screening {\it radiomics};
iii) evaluation results of a proof-of-concept {\it BreastScreening} prototype for two conditions {\it Current} (without AI assistant) and {\it AI-Assisted}; and
iv) evidence from the impact of a {\it Multimodality} and {\it AI-Assisted} strategy in diagnosing and severity classification of lesions.
The above strategies will allow us to conclude about the {\it behaviour} of clinicians when an AI module is present in a diagnostic system.
This {\it behaviour} will have a direct impact in the clinicians workflow that is thoroughly addressed herein.
Our results show a high level of acceptance of {\it AI techniques} from radiologists and point to a significant reduction of cognitive workload and improvement in diagnosis execution.
\end{abstract}

\begin{keywords}

Human-Computer Interaction

Artificial Intelligence

Healthcare

Medical Imaging

Breast Cancer

\end{keywords}

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