Enterprise AI Analysis
Towards Clinically Useful AI: From Radiology Practices in Global South and North to Visions of AI Support
This research highlights that despite AI advancements, its real-world clinical use in radiology remains low due to a mismatch with existing healthcare workflows. A field study across nine medical sites in Denmark and Kenya identified key challenges in chest X-ray practice and envisioned AI futures that align with collaborative clinical work. The findings emphasize that clinical usefulness of AI-based systems depends on configurability and flexibility across clinical site type, professional expertise, and situational/patient contexts, advocating for AI design rooted in practice realities.
Executive Impact
Our comprehensive analysis provides actionable insights for designing AI solutions that genuinely enhance clinical practice and patient outcomes across diverse healthcare settings.
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Radiologists Interviewed
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Medical Sites Studied
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Observation Hours
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Design & Ethics
Explores the broader ethical considerations and design principles for AI in healthcare, focusing on human-centered approaches and responsible AI development.
30%
Radiologist Disagreement Rate in Chest X-ray Interpretation
Despite advancements, radiologists often disagree on chest X-ray interpretations, with rates as high as 30% [37]. This highlights the inherent subjectivity and complexity of diagnostic work, suggesting a need for AI to support rather than replace human expertise.
| Feature | Global North (Denmark) | Global South (Kenya) |
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| Radiologist-to-Population Ratio |
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| AI Adoption Challenges |
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Radiologist P19: AI for Backlog Management
P19, a senior radiologist in Kenya (K5), envisioned AI to 'filter normal radiographs' during high workload. AI would screen incoming chest X-rays, categorizing them as 'normal' or 'abnormal'. 'Normal' cases would go to junior radiologists for verification, while 'abnormal' cases would be prioritised for senior radiologists. This was a pragmatic response to a backlog of 500 unreported radiographs, intended as a temporary tool to manage workload without hindering junior training.
Workflow & Collaboration
Examines the intricate, collaborative nature of radiology practice, detailing the stages of X-ray handling and the dependencies on communication with other healthcare professionals.
Enterprise Process Flow
Selection (Prioritizing)
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Interpretation (Drawing Conclusions)
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Reporting (Contribution)
30 secs - 15 mins
Typical X-ray Assessment Time
Radiologists rapidly assess chest X-rays, with simple cases taking 30 seconds to 5 minutes, and complex ones 5 to 15 minutes. This highlights the need for AI support that integrates seamlessly and efficiently into fast-paced workflows, avoiding additional overhead.
P9's Collaborative Interpretation
P9, a senior radiologist in Denmark (D3), frequently collaborated with colleagues to interpret visually ambiguous findings. Their local PACS's internal messaging system facilitated this by allowing easy sharing of cases. This highlights the inherent collaborative nature of diagnostic work and the need for AI to augment, rather than replace, human interaction and peer consultation in difficult cases.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with a tailored AI implementation, based on our research-backed insights.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach for integrating AI into your enterprise, focusing on clinical utility and seamless adoption, as informed by our research.
Phase 1: Deep Practice Understanding (0-3 Months)
Initial phase focused on ethnographic studies and participatory observations to capture the nuances of radiology workflows across diverse settings. This involves engaging healthcare professionals directly to understand their challenges and needs, ensuring AI development is grounded in real-world practice.
Phase 2: Visioning & Co-Design (3-6 Months)
Collaborative workshops and design sessions with radiologists and other stakeholders to translate identified challenges into actionable AI support visions. Emphasize human-centered AI design, focusing on configurability and flexibility for various clinical contexts, expertise levels, and patient situations.
Phase 3: Prototype & Iterative Development (6-12 Months)
Develop AI prototypes based on co-designed visions, prioritizing features that enhance clinical usefulness and integrate seamlessly into existing workflows. Conduct iterative testing in controlled environments, gathering feedback to refine functionalities and ensure alignment with practical needs.
Phase 4: Clinical Integration & Evaluation (12+ Months)
Deploy refined AI systems into clinical settings for real-world evaluation. Focus on measuring clinical utility, impact on patient outcomes, and user experience, rather than solely technical metrics. Continuously monitor and adapt the system to address emerging needs and ensure long-term clinical relevance and adoption.
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