Artificial intelligence in healthcare management: Clinical applications, evidence, governance, and implementation challenges

Wiky Fhalyang Razaki ()
Additional contact information
Wiky Fhalyang Razaki: Faculty of Public Health, University of Indonesia

Health Economics Insights Journal, 2026, vol. 1, issue 1, 52-63

Abstract: Artificial intelligence (AI) has evolved from experimental computer science into a practical component of modern healthcare systems. AI is increasingly applied in medical imaging, oncology, dermatology, ophthalmology, electronic health record analytics, clinical decision support, drug discovery, patient communication, and hospital management. However, the effectiveness of AI depends not only on algorithmic performance but also on safety, equity, explainability, workflow integration, regulatory oversight, and public trust. This paper presents an integrative narrative review of AI in healthcare based on evidence from peer-reviewed literature indexed in major databases, particularly Scopus and Web of Science. The review focuses on five key themes: diagnostic support, predictive analytics, treatment personalization, generative AI, and responsible implementation. Evidence indicates that AI can achieve specialist-level performance in tasks such as skin cancer classification, diabetic retinopathy detection, breast cancer screening, and medical image interpretation. AI systems using electronic health records can also predict deterioration, mortality, readmission, and acute kidney injury. Recent advances in large language models demonstrate potential for medical question answering, documentation assistance, and patient communication. Despite these benefits, many AI systems remain inadequately validated in real-world settings. Major concerns include algorithmic bias, lack of transparency, privacy risks, automation bias, and weak external validity. Safe AI adoption therefore requires rigorous clinical validation, continuous monitoring, transparent governance, and human-centered implementation that supports rather than replaces professional clinical judgment.

Keywords: artificial intelligence; healthcare management; clinical decision support; machine learning; digital health; ethics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.privietlab.org/download.php?id=12552 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:prv:heijpv:1861

Ordering information: This journal article can be ordered from
https://journal.priv ... IJ/article/view/1861

DOI: 10.55942/heij.v1i1.1861

Access Statistics for this article

More articles in Health Economics Insights Journal from Privietlab Research Center
Bibliographic data for series maintained by Mochammad Fahlevi ().