Artificial intelligence in healthcare: clinical applications, evidence maturity, risks, and life-cycle governance

Artificial intelligence (AI) is increasingly used across clinical, administrative, and public health settings, but technical performance alone does not establish clinical utility, safety, equity, or sustainability.

To critically analyze contemporary healthcare AI applications, characterize source types and non-ordinal evidence profiles in the supporting literature, synthesize key opportunities and risks, and identify governance requirements for clinically useful, safe, equitable, and responsible implementation.

We conducted a critical narrative synthesis using PubMed, the Virtual Health Library, Web of Science, and Scopus as source-discovery tools on July 16, 2026. A curated corpus of 28 bibliographically verifiable publications was assembled judgmentally to provide complementary coverage of clinical applications, evidence characteristics, implementation, risk, and life-cycle governance. Sources were classified by primary design and non-mutually exclusive evidentiary domains, and the evidence described within them was characterized using non-ordinal profile categories. Findings were synthesized qualitatively because of substantial heterogeneity across technologies, settings, and source designs.

The curated corpus addressed diagnosis and imaging, prediction and monitoring, clinical documentation, conversational systems, public health, hospital operations, and specialty applications. Within this assembled corpus, technical-performance/validation and process-utility/workflow domains were represented more often than clinical or patient-safety outcomes, economic outcomes, or post-deployment surveillance; these document-level counts are descriptive and have no inferential weight beyond the selected sources. Reported or proposed benefits included diagnostic support, personalization, documentation, surveillance, and workflow efficiency. Recurrent risks included poor generalizability, bias, miscalibration, hallucinations, opacity, privacy concerns, interoperability problems, inappropriate automation, unclear accountability, and hidden costs.

Within this non-exhaustive corpus, the evidence more consistently supported assistive, context-specific uses than generalized autonomous replacement. Responsible implementation requires a clearly defined intended use, governed data, contextual validation, risk-proportionate human oversight, traceability, equity and safety assessment, continuous monitoring, and procedures to restrict, suspend, or withdraw systems when performance or implementation conditions change.