Developer Insights into Designing AI-Based Computer Perception Tools (2508.21733v1)

Abstract: AI-based computer perception (CP) technologies use mobile sensors to collect behavioral and physiological data for clinical decision-making. These tools can reshape how clinical knowledge is generated and interpreted. However, effective integration of these tools into clinical workflows depends on how developers balance clinical utility with user acceptability and trustworthiness. Our study presents findings from 20 in-depth interviews with developers of AI-based CP tools. Interviews were transcribed and inductive, thematic analysis was performed to identify 4 key design priorities: 1) to account for context and ensure explainability for both patients and clinicians; 2) align tools with existing clinical workflows; 3) appropriately customize to relevant stakeholders for usability and acceptability; and 4) push the boundaries of innovation while aligning with established paradigms. Our findings highlight that developers view themselves as not merely technical architects but also ethical stewards, designing tools that are both acceptable by users and epistemically responsible (prioritizing objectivity and pushing clinical knowledge forward). We offer the following suggestions to help achieve this balance: documenting how design choices around customization are made, defining limits for customization choices, transparently conveying information about outputs, and investing in user training. Achieving these goals will require interdisciplinary collaboration between developers, clinicians, and ethicists.

• The paper highlights that AI-based computer perception tools must deliver explainable outputs for effective clinical decision-making.
• It emphasizes seamless integration with existing clinical workflows to enhance trust and efficiency in healthcare.
• The study recommends balanced customization to boost usability while maintaining system objectivity across diverse clinical settings.

Insights into AI-Based Computer Perception Tools in Clinical Settings

The paper "Developer Insights into Designing AI-Based Computer Perception Tools" (2508.21733) addresses the development of AI-based Computer Perception (CP) technologies intended to revolutionize clinical workflows through the use of mobile sensors for behavioral and physiological data collection. This paper focuses on the perspectives of developers on integrating CP tools into healthcare, emphasizing the balance between innovative tool design and alignment with current clinical paradigms.

Key Findings and Design Considerations

Output Context and Explainability

Developers underscore the importance of ensuring that AI-generated outputs are interpretable and actionable for clinicians. Clinicians act as intermediaries, translating CP insights into actionable care decisions. Hence, outputs must be contextually relevant and equipped with explainability features allowing end-users to understand both the "what" and "why" behind AI recommendations. This includes user-friendly summaries for quick decision-making, as well as providing detailed technical explanations for in-depth analysis.

Alignment with Clinical Workflows

The adoption of CP tools hinges on seamless integration into pre-existing clinical workflows. Developers stress the need for CP outputs to align with clinicians' expectations and medical paradigms to avoid distrust or skepticism. CP systems should complement clinicians' workflows by optimizing diagnostic precision and integrating with electronic health records and IT systems, enhancing their acceptability and utility in medical practice.

Optimal Customization

While customization is seen as a pathway to enhancing utility and user acceptance, developers caution against over-customization that may bias system outputs. It is critical to balance user-specific customization to maintain system objectivity and ensure broad applicability across diverse clinical settings. CP tools should remain versatile, offering customization that enhances integration with workflows without constraining clinical reasoning.

Balancing Innovation with Responsibility

The paper addresses the tension between aligning with current clinical practices and fostering innovation. Developers are tasked with designing CP tools that extend beyond the conventional clinical insights, thereby challenging established paradigms. However, there remains the necessity to ensure these innovative insights are communicated effectively to clinicians and patients, facilitating acceptance and integration into care.

Implications and Recommendations

The Innovation-Implementation Paradox

An inherent paradox exists between pushing new technological frontiers and maintaining alignment with the status quo to encourage uptake and trust. Successful integration of CP tools requires transparency and explainability, allowing clinicians to comprehend AI outputs in a manner that supports their decision-making processes. This paradox points to the need for CP tool designers to support layered insights for diverse user expertise and to implement clinician-targeted user training to promote system comprehension.

Customization Challenges

Customization appears central to developer concerns, highlighting its dual potential to aid or hinder usability and objectivity. Developers advocate for transparent documentation of customization decisions and clear delineations of reasonable customization limits to balance personalization with system-wide utility.

Recommendations for Responsible Integration

Developers propose several strategies to achieve a balance between innovation and clinical utility. These include:

• Documenting design decisions and customization strategies to clarify the rationales behind them.
• Defining limits for customization to avoid undermining generalizability and usability.
• Building transparency and expanding explainability features within CP systems.
• Investing in clinician training to better comprehend and utilize CP tools within clinical settings.

Conclusion

This paper articulates the complex balance developers must navigate between innovation, usability, and ethical responsibility in designing CP tools for healthcare integration. Developers are positioned as ethical stewards committed to designing tools that are rigorously aligned with clinical needs while fostering innovation. Achieving this balance mandates interdisciplinary collaboration to ensure the CP systems uphold standards of care and trustworthiness. Furthermore, research must continue to elucidate how design decisions influence clinical practices and support the ethical evolution of CP technologies in healthcare.