BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Calisto, Francisco Maria; Santiago, Carlos; Nunes, Nuno; Nascimento, Jacinto C.

doi:10.1016/j.artmed.2022.102285

Cited by 77 publications

(27 citation statements)

References 49 publications

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“…When properly trained and validated, such methods can exceed human performance and therefore lead to better patient outcomes [ 52 ]. Moreover, AI techniques can aid clinicians and improve their performance, while decreasing time spent working [ 53 ]. In the setting of breast cancer lymph node prediction, we investigated three main classification modalities: (1) radiomics, (2) clinicopathological data, and (3) a combined approach.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Vrdoljak

Krešo

Kumrić

et al. 2023

Cancers

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Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Vrdoljak

Krešo

Kumrić

et al. 2023

Cancers

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“…There is also a need to improve evaluation and reporting [83,84]. Study designs were largely quantitative and aimed at examining effects on decision-making by comparing system performance against a gold standard (e.g., [25,56]); or by comparing clinician performance with and without AI assistance (e.g., [20,21,32]). Few studies used randomised trial designs, opting instead for designs such as weaker historical case controls.…”

Section: Discussionmentioning

confidence: 99%

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

Magrabi,

Lyell,

Coiera

2023

Yearb Med Inform

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Aims and objectives: To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings. Method: PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised. Results: AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making. Conclusion: AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.

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“…Efforts to support human decision-making with ML have often investigated how AI and humans can work complementary with one another, such as having ML models learn when to defer to humans [53,71] or designing AI analytic tools to augment human intuition [15,16,47]. In some instances, researchers have tested if ML models can make better predictions than humans, even finding in some cases that accounting for additional human unpredictability, models can outperform human decision makers [54].…”

Section: Pursuing Ai As a Complement To Human Decision-makingmentioning

confidence: 99%

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

Zhang,

Walker,

Nguyen

et al. 2023

Preprint

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Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders-decision makers (faculty) and decision subjects (students)-use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

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BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Cited by 77 publications

References 49 publications

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

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