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Al-NafJan, A. (2025). Artificial Intelligence in Predictive Healthcare: A Systematic Review. MDPI, 14(19). https://doi.org/10.3390/jcm14196752 This journal article explores the application of AI in healthcare, primarily focusing on patient flow, ICU demand predictions, and operational efficiency. It combines findings from 22 studies and emphasizes how machine learning models improve hospital resource allocation and also reduces overcrowding. Highly relevant for understanding AI’s role in hospital logistics and staffing optimization.

 

Fell, C. (2023, March 8). Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence. PLOS One. https://doi.org/10.1371/journal.pone.0282577 - We see within this article, Fell investigates the use of AI in pathology, specifically for detecting malignancies in cancer biopsy slides. The study demonstrates how deep learning can enhance diagnostic accuracy and reproducibility. While not really focused on hospital operations, it underscores AI’s clinical application and potential to enhance diagnostic operations.

 

Ferreira, F. (2025, June 6). AI-Driven Drug Discovery: A Comprehensive Review. ACS Omega, 10(23). https://pubs.acs.org/doi/pdf/10.1021/acsomega.5c00549 This journal covers AI’s impact on pharmaceutical development, including predictive modeling for drug regulations and safety. Though primarily centered on drug discovery, it also contributes to the broader understanding of AI’s predictive capabilities and its direct effects on hospital treatment protocols and inventory planning.

 

Khosravi, P. (2025, July 2). Artificial Intelligence–Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality. American Association for Cancer Research. https://doi.org/10.1158/0008-5472.CAN-24-3630 - Within this journal, we see an overview of AI-enhanced cancer diagnostics. The article highlights how AI greatly improves diagnostic precision and workflow efficiency, which indirectly supports hospital management by reducing diagnostic delays and optimizing care pathways, ultimately having a direct effect on patient satisfaction.

 

Price, H. R., Collier, A. C., & Wright, T. E. (2023, December 13). Liability for harm caused by AI in healthcare: an overview of the core legal concepts. Frontiers. Retrieved October 3, 2025, from https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1297353/full This article explores the legal implications of AI in healthcare, including liability frameworks and risk management. This is crucial for hospital administrators that may be considering the adoption of AI, as it outlines the various structures needed to handle legal exposure.

 

Shin, Y. (2025, April 16). Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care—Innovations, Limitations, and Future Directions. MDPI, 15(4), 654. https://doi.org/10.3390/life15040654 In this journal, Shin discusses AI’s role in quality assurance across diagnostics and surgical procedures. The article highlights how predictive analytics can reduce errors and improve patient outcomes, which tends to align with hospital goals of efficiency and safety.

 

Suresh, & Sridhar. (n.d.). 2. https://www.igi-global.com/viewtitlesample.aspx?id=353065&ptid=348064&t=machine+learning+applications+in+healthcare%3a+improving+patient+outcomes%2c+diagnostic+accuracy%2c+and+operational+efficiency Yang, Y. (n.d.). Application of artificial intelligence medical imaging aided diagnosis system in the diagnosis of pulmonary nodules. BioMed Central. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-03009-4

 

Zavaleta-Monestel, E. (2025, July 16). Integrating artificial intelligence into the hospital supply chain to ensure the availability of medications. European Journal of Hospital Pharmacy. https://doi.org/10.1136/ejhpharm-2025-004635

 

Akingbola, A. (2024). Artificial Intelligence and the Dehumanization of Patient Care. ScienceDirect, 3. https://www.sciencedirect.com/science/article/pii/S2949916X24000914 Annotation: this survey examines people's trust in the use of AI in healthcare and identifies various factors like trust, transparency, and fairness and also provides evidence on patient perceptions

 

Alowais, S. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BioMed Central, 23(689). https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-023-04698-z Annotations: this article explain the use of AI driven patient conversations to manage diseases, personalization, and ethical concerns by giving concrete examples

 

Anisha, S. (2024). Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review. NIH National Library of Medicine. https://pubmed.ncbi.nlm.nih.gov/39012688/ Annotation: this article goes into concerns of depersonalization of care and supports the discussion of ethical and social implications of AI use.

 

Chusteki, M. (n.d.). Benefits and Risks of AI in Health Care: Narrative Review. PubMed Central, 13. https://doi.org/10.2196/53616 Annotation: This article examines the role of AI in treatment, diagnostics, and patient engagement, really highlighting its benefits while also giving examples of clinical applications

 

Nong, P. (2025, February 14). Patients’ Trust in Health Systems to Use Artificial Intelligence. JAMA Network, 8(2). https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2830240 Annotation: this article provides the statistical analysis of various patient concerns and also positive usage of AI in healthcare by adding evidence based information

 

Ogundare, O. (2025). Integrated artificial intelligence in healthcare and the patient's experience of care. PubMed, 15(1). https://doi.org/10.1038/s41598-025-07581-7 Annotation: this journal explains the benefits of efficiency and personalization of AI in healthcare while also while also including the risk of bias and security concerns when using AI by also providing a fair perspective of security aspects as well as ethical and social implications