Understanding Providers’ Attitude Toward AI in India’s Informal Health Care Sector: Survey Study
Bibliographic record
Abstract
Background: Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expensive, and inaccessible, making chest x-rays augmented with artificial intelligence (AI) a promising solution. However, whether providers are willing to adopt AI is not apparent. Objective: The study seeks to understand the attitude of Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy (AYUSH) and informal health care providers, who we jointly call AIPs, toward adopting AI for TB diagnosis. We chose to study these providers as they are the first point of contact for a majority of TB patients in India. Methods: We conducted a cross-sectional survey of 406 AIPs across the states of Jharkhand (162 participants) and Gujarat (244 participants) in India. We designed the survey questionnaire to assess the AIPs' confidence in treating presumptive TB patients, their trust in local radiologists' reading of the chest x-ray images, their beliefs regarding the diagnostic capabilities of AI, and their willingness to adopt AI for TB diagnosis. Results: We found that 93.7% (270/288) of AIPs believed that AI could improve the accuracy of TB diagnosis, and for those who believed in AI, 71.9% (194/270) were willing to try AI. Among all AIPs, 69.4% (200/288) were willing to try AI. However, we found significant differences in AIPs' willingness to try AI across the 2 states. Specifically, in Gujarat, a state with better and more accessible health care infrastructure, 73.4% (155/211) were willing to try AI, and in Jharkhand, 58.4% (45/77) were willing to try AI. Moreover, AIPs in Gujarat who showed higher trust in the local radiologists were less likely to try AI (odds ratio [OR] 0.15, 95% CI 0.03-0.69; P=.02). In contrast, in Jharkhand, those who showed higher trust in the local radiologists were more likely to try AI (OR 2.11, 95% CI 0.9-4.93; P=.09). Conclusions: While most AIPs believed in the potential benefits of AI-based TB diagnoses, many did not intend to try AI, indicating that the expected benefits of AI measured in terms of technological superiority may not directly translate to impact on the ground. Improving beliefs among AIPs with poor access to radiology services or those who are less confident of diagnosing TB is likely to result in a greater impact of AI on the ground. Additionally, tailored interventions addressing regional and infrastructural differences may facilitate AI adoption in India's informal health care sector.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".