SynthesisF1000Research2025
Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions.
Synthesis in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare-one of the core pillars of the United Nations' Sustainable Development Goals-is being reshaped by the rapid spread of AI. While AI adoption offers substantial opportunities for innovation, it also introduces new challenges and disrupts established healthcare practices. Hence, understanding and documenting how healthcare organizations develop AI-related capabilities and translate them into organizational value is important. This study conducts a systematic literature review to examine how healthcare organizations build and leverage AI-enabled capabilities to create value. Following the PRISMA guidelines and drawing on dynamic capability theory as the analytical framework, I conducted a structured literature search using the keywords "artificial intelligence" and "healthcare capability." The search encompassed open-access publications from PubMed and IEEE Xplore, as well as institutionally accessed studies from the AIS eLibrary, focusing on articles published between 2015 and 2025. Applying predefined inclusion and exclusion criteria resulted in a final sample of 102 articles. I employed qualitative analysis to systematically examine the selected studies. The analysis identifies key AI tools in healthcare, their underlying micro-foundations, the AI-enabled capabilities they support, the resulting healthcare outcomes, and the challenges shaping AI-enabled healthcare. Building on these findings, I propose a process model that explains how AI tools and micro-foundations enable sensing, seizing, and transforming capabilities, which in turn drive AI-enabled healthcare outcomes. These outcomes recursively reinforce and further develop AI-enabled healthcare capabilities. This study contributes to the literature on AI and dynamic capabilities in healthcare by clarifying the mechanisms through which AI creates value. From a practical perspective, it offers actionable insights for healthcare organizations seeking to operationalize AI effectively by clarifying how AI strengthens and extends healthcare capabilities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.