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Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine
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The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review.
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“Previous works have evaluated XAI in AI-based CDSS, but only as a secondary aspect [99,101,103].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [101]
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Metadata
- Authors
- Rundo, L., Pirrone, R., Vitabile, S., Sala, E., & Gambino, O.
- Affiliations
- University of Cambridge (GB)
- University of Palermo (IT)
- Corresponding author
- Gambino, Orazio
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 108
- Pages
- 103479
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3036614995
- Cited by
- 113
- References
- 138
- Citations by year
- 2026: 18 · 2025: 12 · 2024: 29 · 2023: 16 · 2022: 16 · 2021: 20 · 2020: 2
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- Apollo
- Funders
- Cancer Research UK
- Mark Foundation For Cancer Research
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- human-computer interaction, clinical decision-making, clinical decision support systems, precision medicine, clinical workflow, electronic health records, visualization, usability, medical devices, evidence-based reasoning, AI-assisted decision support
- MeSH terms
- Artificial Intelligence, Computers, Humans, Decision Support Systems, Clinical, Workflow, Precision Medicine
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