Aleksandar Babic

Tita A. Bach, Jenny K. Kristiansen, Aleksandar Babic et al.

Ensuring quality human-AI interaction (HAII) in safety-critical industries is essential. Failure to do so can lead to catastrophic and deadly consequences. Despite this urgency, existing research on HAII is limited, fragmented, and inconsistent. We present here a survey of that literature and recommendations for research best practices that should improve the field. We divided our investigation into the following areas: 1) terms used to describe HAII, 2) primary roles of AI-enabled systems, 3) factors that influence HAII, and 4) how HAII is measured. Additionally, we described the capabilities and maturity of the AI-enabled systems used in safety-critical industries discussed in these articles. We found that no single term is used across the literature to describe HAII and some terms have multiple meanings. According to our literature, seven factors influence HAII: user characteristics (e.g., user personality), user perceptions and attitudes (e.g., user biases), user expectations and experience (e.g., mismatched user expectations and experience), AI interface and features (e.g., interactive design), AI output (e.g., perceived accuracy), explainability and interpretability (e.g., level of detail, user understanding), and usage of AI (e.g., heterogeneity of environments). HAII is most measured with user-related subjective metrics (e.g., user perceptions, trust, and attitudes), and AI-assisted decision-making is the most common primary role of AI-enabled systems. Based on this review, we conclude that there are substantial research gaps in HAII. Researchers and developers need to codify HAII terminology, involve users throughout the AI lifecycle (especially during development), and tailor HAII in safety-critical industries to the users and environments.

Taridzo Chomutare, Aleksandar Babic, Laura-Maria Peltonen et al.

Background: Centralized collection and processing of healthcare data across national borders pose significant challenges, including privacy concerns, data heterogeneity and legal barriers. To address some of these challenges, we formed an interdisciplinary consortium to develop a feder-ated health data network, comprised of six institutions across five countries, to facilitate Nordic-Baltic cooperation on secondary use of health data. The objective of this report is to offer early insights into our experiences developing this network. Methods: We used a mixed-method ap-proach, combining both experimental design and implementation science to evaluate the factors affecting the implementation of our network. Results: Technically, our experiments indicate that the network functions without significant performance degradation compared to centralized simu-lation. Conclusion: While use of interdisciplinary approaches holds a potential to solve challeng-es associated with establishing such collaborative networks, our findings turn the spotlight on the uncertain regulatory landscape playing catch up and the significant operational costs.

Tita Alissa Bach, Aleksandar Babic, Narae Park et al.

The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.

Taridzo Chomutare, Therese Olsen Svenning, Miguel Ángel Tejedor Hernández et al.

\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

Vibeke Binz Vallevik, Serena Elizabeth Marshall, Aleksandar Babic et al.

Synthetic data is emerging as a cost-effective solution necessary to meet the increasing data demands of AI development, created either from existing knowledge or derived from real data. The traditional classification of synthetic data types into hybrid, partial or fully synthetic datasets has limited value and does not reflect the ever-increasing methods to generate synthetic data. The generation method and their source jointly shape the characteristics of synthetic data, which in turn determines its practical applications. We make a case for an alternative approach to grouping synthetic data types that better reflect privacy perspectives in order to facilitate regulatory guidance in the generation and processing of synthetic data. This approach to classification provides flexibility to new advancements like deep generative methods and offers a more practical framework for future applications.

Vibeke Binz Vallevik, Aleksandar Babic, Serena Elizabeth Marshall et al.

Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. In response to privacy concerns and regulatory requirements, using synthetic data has been suggested. Synthetic data is created by training a generator on real data to produce a dataset with similar statistical properties. Competing metrics with differing taxonomies for quality evaluation have been suggested, resulting in a complex landscape. Optimising quality entails balancing considerations that make the data fit for use, yet relevant dimensions are left out of existing frameworks. We performed a comprehensive literature review on the use of quality evaluation metrics on SD within the scope of tabular healthcare data and SD made using deep generative methods. Based on this and the collective team experiences, we developed a conceptual framework for quality assurance. The applicability was benchmarked against a practical case from the Dutch National Cancer Registry. We present a conceptual framework for quality assurance of SD for AI applications in healthcare that aligns diverging taxonomies, expands on common quality dimensions to include the dimensions of Fairness and Carbon footprint, and proposes stages necessary to support real-life applications. Building trust in synthetic data by increasing transparency and reducing the safety risk will accelerate the development and uptake of trustworthy AI tools for the benefit of patients. Despite the growing emphasis on algorithmic fairness and carbon footprint, these metrics were scarce in the literature review. The overwhelming focus was on statistical similarity using distance metrics while sequential logic detection was scarce. A consensus-backed framework that includes all relevant quality dimensions can provide assurance for safe and responsible real-life applications of SD.

Andrew Gilbert, Marit Holden, Line Eikvil et al.

Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give important insight into myocardial motion and blood flow providing clinicians with parameters for diagnostic decision making. Many of these measurements can currently be performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline. However, full automation is not yet available because the user must manually select which measurement should be performed on each image. In this work we develop a convolutional neural network (CNN) to automatically classify cardiac Doppler spectra into measurement classes. We show how the multi-modal information in each spectral Doppler recording can be combined using a meta parameter post-processing mapping scheme and heatmaps to encode coordinate locations. Additionally, we experiment with several state-of-the-art network architectures to examine the tradeoff between accuracy and memory usage for resource-constrained environments. Finally, we propose a confidence metric using the values in the last fully connected layer of the network. We analyze example images that fall outside of our proposed classes to show our confidence metric can prevent many misclassifications. Our algorithm achieves 96% accuracy on a test set drawn from a separate clinical site, indicating that the proposed method is suitable for clinical adoption and enabling a fully automatic pipeline from acquisition to Doppler spectrum measurements.