Large scale implementation of DP for clinical diagnoses: experience, challenges, and lessons learned
Bibliographic record
Abstract
Implementing DP on a large scale is a complex, multi-dimensional process that requires strategic planning, technological adaptation, and change management. We provide a detailed account of the full-scale implementation of DP at the University Health Network (UHN), a multi-site tertiary clinical center in Canada, highlighting practical lessons learned, ongoing challenges, and mitigation strategies. A phased implementation approach was adopted, involving pre-implementation planning, procurement, infrastructure development, and optimized validation protocols. Significant focus was placed on technical considerations, including system interoperability, storage capacity, and image quality. Procurement was structured to ensure vendor neutrality and long-term sustainability.A critical component of the implementation was "change management", addressing resistance to change through extensive training, real-time troubleshooting, utilizing "super users" as change champions. Attention was paid to pathologist office configuration. A dual workflow model, with simultaneous access to both glass and digital slides, facilitated smoother transition. As of this writing all histopathology H&E cases and tissue hematopathology are being scanned. Efforts to implement digital liquid hematopathology and cytopathology are ongoing. The financial implications of DP implementation were evaluated, including direct and indirect costs. While initial investments in scanners, storage, and software infrastructure were substantial, long-term savings are anticipated through increased efficiency, reduced physical slide storage, enhanced workload distribution and the integration of AI-based tools. Continuous monitoring and feedback were established to assess system performance and address emerging challenges. Scalability and future applications of DP remain a priority. The adoption of AI-driven pathology tools, remote diagnostics, and cross-institutional data sharing are anticipated to further enhance the value of DP. UHN's experience underscores the importance of a structured, multidisciplinary approach to DP implementation. Our experience offers a realistic and evolving roadmap for institutions considering DP adoption. We provide practical guidance, highlight persistent challenges and emphasize the importance of continuous evaluation and adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".