Comparative Outcomes of AI-Assisted Diagnosis vs. Traditional Diagnosis in Primary Care Settings
DOI:
https://doi.org/10.70749/ijbr.v3i9.2336Keywords:
Artificial intelligence, diagnostic accuracy, primary care, traditional diagnosis, patient satisfaction, healthcare efficiency.Abstract
Background: To compare the outcomes of AI-assisted diagnosis with traditional diagnostic approaches in primary care settings, focusing on diagnostic accuracy, efficiency, cost, and patient satisfaction. Methods: A cross-sectional comparative study was conducted between January 2024 and January 2025 at Primary Care Setup in Lahore. A total of 72 patients were equally divided into two groups: AI-assisted diagnosis (n=36) and traditional physician diagnosis (n=36). Data on demographics, presenting complaints, diagnostic process measures, and patient outcomes were recorded. Statistical comparisons were made using independent t-tests and Chi-square tests, with p < 0.05 considered significant. Results: AI-assisted diagnosis demonstrated higher diagnostic accuracy (88.9% vs. 72.2%, p = 0.04), lower misdiagnosis rates, and greater patient satisfaction (83.3% vs. 61.1%, p = 0.03). Mean time to diagnosis (12.4 ± 3.5 vs. 21.7 ± 4.2 minutes, p < 0.001), number of tests ordered, and diagnostic costs were significantly lower in the AI group. Clinician confidence scores were also higher with AI support (p = 0.03). Conclusion: AI-assisted diagnostic systems significantly improved accuracy, efficiency, and patient satisfaction compared with traditional approaches. Integration of AI into primary care may enhance clinical decision-making and optimize resource utilization.
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References
1. Huang, G., et al., A systematic review and meta-analysis of diagnostic performance and physicians’ perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules. 2021. 13(8): p. 4797.
https://doi.org/10.21037/jtd-21-810
2. Hah, H. and D.S.J.J.o.M.I.R. Goldin, How clinicians perceive artificial intelligence–assisted technologies in diagnostic decision making: Mixed methods approach. 2021. 23(12): p. e33540.
3. Li, X., et al., Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study. 2021. 21(1): p. 237.
https://doi.org/10.1186/s12913-021-06248-z
4. Hsieh, P.-J.J.C.i.H.B., Determinants of physicians’ intention to use AI-assisted diagnosis: An integrated readiness perspective. 2023. 147: p. 107868.
https://doi.org/10.1016/j.chb.2023.107868
5. Tang, H., et al. AI-assisted diagnosis and decision-making method in developing countries for osteosarcoma. in Healthcare. 2022. MDPI.
https://doi.org/10.3390/healthcare10112313
6. Chan, P.Z., et al., Diagnostic test accuracy of artificial intelligence-assisted detection of acute coronary syndrome: a systematic review and meta-analysis. 2023. 167: p. 107636.
https://doi.org/10.1016/j.compbiomed.2023.107636
7. Wang, J.-J.J.E.E.T.o.e.-L., The Power of AI-Assisted Diagnosis. 2022. 8(4).
https://doi.org/10.4108/eetel.3772
8. Ru, X., et al., Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial. 2022. 23(1): p. 316.
https://doi.org/10.1186/s13063-022-06250-8
9. Seol, H.Y., et al., Artificial intelligence-assisted clinical decision support for childhood asthma management: a randomized clinical trial. 2021. 16(8): p. e0255261.
https://doi.org/10.1371/journal.pone.0255261
10. Shelke, Y.P., et al., Applications of artificial intelligence in microbial diagnosis. 2023. 15(11).
https://doi.org/10.7759/cureus.49366
11. Hah, H. and D.J.H.I.J. Goldin, Moving toward AI-assisted decision-making: Observation on clinicians’ management of multimedia patient information in synchronous and asynchronous telehealth contexts. 2022. 28(1): p. 14604582221077049.
https://doi.org/10.1177/14604582221077049
12. Gill, A.Y., et al., Revolutionizing healthcare: how machine learning is transforming patient diagnoses-a comprehensive review of AI's impact on medical diagnosis. 2023. 2(10): p. 1638-1652.
https://doi.org/10.58344/jws.v2i10.449
13. Kiran, N., et al., Digital pathology: transforming diagnosis in the digital age. 2023. 15(9).
https://doi.org/10.7759/cureus.44620
14. Shaheen, M.Y.J.S.P., Applications of Artificial Intelligence (AI) in healthcare: A review. 2021.
https://doi.org/10.14293/s2199-1006.1.sor-.ppvry8k.v1
15. Väänänen, A., et al., AI in healthcare: A narrative review. 2021. 10: p. 6.
https://doi.org/10.12688/f1000research.26997.1
16. Cheng, M., et al., Promoting healthcare workers’ adoption intention of artificial-intelligence-assisted diagnosis and treatment: the chain mediation of social influence and human–computer trust. 2022. 19(20): p. 13311.
https://doi.org/10.3390/ijerph192013311
17. Khanna, N.N., et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. in Healthcare. 2022. MDPI.
https://doi.org/10.3390/healthcare10122493
18. Kelvin-Agwu, M., et al., Development of AI-Assisted Wearable Devices for Early Detection of Respiratory Diseases. 2023. 4(1): p. 967-974.
https://doi.org/10.54660/.ijmrge.2023.4.1.967-974
19. Lam, T.Y., et al., Randomized controlled trials of artificial intelligence in clinical practice: systematic review. 2022. 24(8): p. e37188.
20. Park, S.H., et al., Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis. 2023. 306(1): p. 20-31.
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