Frontline Health Workers’ Perspectives of the WHO skin NTD app in Kenya – a Qualitative Study on AI-embedded mHealth Implementation (Preprint)
Bibliographic record
Abstract
Background: Skin neglected tropical diseases (NTDs) pose significant diagnostic and management challenges in resource-limited settings due to constrained dermatological expertise, frontline health worker (FHW) training, and limited access to diagnostic resources. Mobile health apps with artificial intelligence (AI)-enabled diagnostic imaging capabilities have the potential to enhance clinical decision-making and professional development at the primary care level. The World Health Organization (WHO) skin NTD mobile app uses convolutional neural networks to analyze images of skin lesions and generate differential diagnoses, intended to be used alongside clinical history and examination, to support FHWs in identifying 12 skin NTDs and 24 common skin conditions. Beyond clinical decision support, the app also aims to upskill FHWs in the recognition and management of these diseases. However, the success of such tools depends on understanding users' needs and the realities of implementation in diverse clinical contexts. Objective: This study aimed to explore FHWs' perspectives on the real-world use and impact of the AI-embedded WHO Skin NTDs app on diagnostic workflows, dermatological understanding, clinical decision-making, and FHW-patient interactions across diverse health care delivery settings in Kenya. Methods: This qualitative study involved 36 FHWs from 5 skin NTD-endemic counties in Kenya. Following a training workshop, FHWs integrated the app into routine clinical workflows from June to October 2024. Data were collected through 15 semistructured interviews (each 30-45 minutes) and 4 focus group discussions (1-1.5 hours) exploring FHW experiences across diverse health care delivery contexts. All sessions were audio-recorded, transcribed verbatim, and thematically analyzed using NVivo (QSR International), using a bottom-up inductive coding approach. Results: FHWs reported that the app facilitated a shift from habitual referral to more proactive case management at the local-level facility, reinforcing clinical ownership and positioning them as local dermatology reference points. It was perceived to enhance diagnostic confidence, strengthen patient trust, and encourage community engagement. Some FHWs described how the app helped mitigate situations for patient stigma due to decreased reliance on public colleague consultations. However, technical limitations (eg, internet dependency and algorithmic errors) constrained consistent use. While most FHWs used the app in line with its intended role as an assistive tool, a minority reported situations of diagnostic deferral to the AI output, highlighting potential considerations of clinical autonomy. Conclusions: The WHO Skin NTDs app shows strong potential to strengthen frontline dermatological capacity that aligns with WHO strategies to decentralize NTD care and promote "skin health for all." Our findings underscore the importance of embedding such tools within ethical and pedagogical frameworks that protect clinical autonomy and foster sustainable capacity building. Further research will examine real-world use in situ to guide context-specific governance, ensuring that this AI-embedded tool enhances-rather than displaces-clinical reasoning and epistemic authority.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".