Literature Review: Artificial Intelligence in Healthcare
Artificial Intelligence (AI) has emerged as a transformative force in modern healthcare, enabling advancements in diagnostics, personalized medicine, clinical workflows, and administrative efficiency. The selected body of literature offers a comprehensive view of the capabilities, challenges, and real-world applicability of AI technologies in clinical settings.

Academic Reviews

1. Evaluating AI Performance in Healthcare Exams
   Waldock et al. (2024) conducted a meta-analysis that reviewed 32 studies evaluating large language models (LLMs) on medical exams such as the USMLE. The study found that models like ChatGPT reached an average of 64% accuracy, outperforming the general mean of 61%. These findings suggest that LLMs are approaching competency levels suitable for medical education support. However, the study highlights a critical gap: these models are rarely tested in real clinical environments. To address safety and policy gaps, the authors propose the RUBRICC framework for responsible AI deployment.

2. Bias Detection and Mitigation in EHR-based AI Systems
   Chen et al. (2023) systematically reviewed strategies for detecting and reducing bias in AI models trained on Electronic Health Records (EHRs). They identified six common types of biases and noted the diversity of mitigation strategies such as data resampling, reweighting, and adversarial debiasing. Despite these efforts, a major limitation is the absence of standardized methods across studies, raising concerns about reproducibility and fairness in clinical AI deployment.

AI in Wound Assessment
Anisuzzaman et al. (2020) reviewed over 115 papers exploring AI systems for wound classification and severity detection. The authors found that computer vision-based AI systems have achieved high accuracy in wound segmentation and diagnosis, showing promise for mobile and telemedicine applications. However, the lack of clinical trials and inconsistent metrics across studies limit the generalizability of these results.

System and Implementation Reviews
4. Barriers and Facilitators in Clinical AI Adoption
Li et al. (2021) used the CFIR (Consolidated Framework for Implementation Research) to synthesize findings from 19 studies on clinical AI implementation. They found that leadership engagement and user training are key facilitators, whereas data quality and system interoperability are significant barriers. A notable limitation is the focus on late-stage implementation rather than planning and development phases.

5. Implementing AI in Hospitals for a Learning Health System (LHS)
   Waldrop et al. (2024) investigated AI integration within hospitals aiming to become Learning Health Systems. The review highlighted the importance of interoperable EMRs (Electronic Medical Records) and organizational readiness. Although the study categorized enablers and barriers across people, processes, technologies, and information, it lacked specific roadmaps for transitioning traditional systems into fully AI-enabled LHS environments.

6. Multi-Level AI Adoption Factors in Healthcare Institutions
   A 2023 study published in Technological Society presented a framework based on 130 studies, outlining the macro (e.g., regulatory, policy), meso (e.g., hospital system), and micro (e.g., clinician) level factors that influence AI adoption. While comprehensive in scope, the study was largely theoretical and recommended future research to validate the framework using real-world case studies.

Summary and Research Gaps
Across the reviewed literature, it is evident that AI has strong potential to enhance healthcare outcomes, particularly through diagnostic assistance, workflow automation, and personalized care. However, three core challenges persist:

Validation in Real Clinical Environments: Most studies are conducted in simulated or academic settings without real-world testing.

Standardization and Fairness: There is a lack of unified frameworks to address bias, ethics, and transparency.

System-Level Integration: Organizational readiness and infrastructure (e.g., EMRs, data interoperability) remain significant hurdles for implementation.

Future Research Directions
Development of regulatory-compliant clinical trials for AI tools.

Longitudinal studies to assess the real-world impact of AI on patient outcomes.

Cross-disciplinary research combining AI development with behavioral science and healthcare management.

Authors
Sushant subedi
Sampanna Timalsina
Gaurav koirala