Page 348 - “O‘ZBEKISTON – 2030 STRATEGIYASI: AMALGA OSHIRILAYOTGAN ISLOHOTLAR TAHLILI, MUAMMOLAR VA YECHIMLAR”

3. Case Study Analysis
Three representative case studies were selected for in-depth evaluation:

Table 1. Comparative Performance of Selected AI Systems in Healthcare

System Domain Accuracy (%) Reference
Google Health AI Breast Cancer Detection 94.5 McKinney et al., 2020
IBM Watson Oncology Therapy Recommendation 90.0 Somashekhar et al., 2018
DeepMind Retinopathy Detection 92.1 WHO, 2021

• Google Health AI – breast cancer screening model with a documented
accuracy of 94.5% (McKinney et al., 2020 [1]).
• IBM Watson for Oncology – therapeutic recommendation system with 90%
concordance to clinical guidelines (Somashekhar et al., 2018 [7]).
• DeepMind Retinopathy Detector – achieving 92.1% accuracy in
ophthalmological diagnostics (WHO, 2021 [8]).
Each case was analyzed to assess real-world applicability, integration
challenges, and human–AI interaction patterns within clinical settings.

4. Limitations
The study’s main limitation is its reliance on secondary data, introducing
possible publication bias and regional inconsistencies. Differences in healthcare
infrastructure and demographics further restrict generalizability, though consistent
inclusion criteria partially mitigate these effects [9].
Using a mixed-method design, the research reviewed literature from 2018–
2024, including Nature, JCO Global Oncology, and WHO reports [10]. Comparative
analyses and case studies—such as IBM Watson for Oncology and Google Health—
assessed AI performance and interpretability. Despite its rigor, dependence on
existing studies and heterogeneous healthcare systems remains a key constraint.
A central challenge concerns interpretability: deep learning (DL) models often
act as opaque “black boxes,” hindering clinicians’ trust and validation. Advancing
Explainable AI (XAI) is crucial for transparency and safety.
Smith et al. highlight that full explainability in AI-CDSS is still elusive, advocating
a precautionary approach—AI outputs should be interpreted only by qualified
clinicians to ensure ethical and responsible use.

ANALYSIS
The analysis of Artificial Intelligence (AI) applications in Clinical Decision Support
Systems (CDSS) identifies key advantages and persisting challenges. AI enhances
diagnostic precision by reducing false negatives in medical imaging, supports early
detection of critical conditions through predictive modeling, and improves
therapeutic compliance with international guidelines. Despite these benefits, issues
of data quality, algorithmic bias, and limited transparency remain significant barriers.
Comparative evidence confirms consistent diagnostic improvement but emphasizes
continuing difficulties in model interpretability.
While AI substantially strengthens CDSS performance and healthcare
outcomes, its clinical integration demands careful management of technical, ethical,
and organizational complexities. Core challenges involve model explainability, bias 345
mitigation, and regulatory compliance, which collectively determine the reliability

V SHO‘BA:

Raqamli transformatsiya va ilg‘or boshqaruv tizimlarini joriy etish samaradorligi

https://www.asr-conference.com