ReviewCureus2026
Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Healthcare Goes Digital: mHealth, eHealth, Artificial Intelligence, and Emerging Digital Technologies Within Digital Health Transformation.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly integrated into modern healthcare, with rapidly expanding applications in medical diagnostics, laboratory medicine, rehabilitation, and patient-centered digital health solutions. The aim of this narrative review is to provide a critically curated overview of current clinical applications of AI across the healthcare continuum, from diagnosis to rehabilitation, while highlighting their clinical benefits, limitations, and implementation challenges. A targeted narrative literature search was conducted using major biomedical databases, including PubMed/MEDLINE, Scopus, Web of Science, and Embase, with emphasis on recent and influential studies published primarily over the past decade. Evidence was qualitatively synthesized across key clinical domains, including diagnostic imaging, laboratory diagnostics, rehabilitation technologies, and conversational agents. The reviewed literature indicates that AI systems can achieve diagnostic performance comparable to healthcare professionals in selected, well-defined tasks, particularly within imaging-based specialties such as radiology, mammography, ophthalmology, dermatology, and digital pathology, predominantly under retrospective or controlled study conditions. In laboratory medicine, AI-based tools support workflow optimization, result interpretation, and clinical decision support, while in rehabilitation, AI-enabled systems - including robotics, motion analysis platforms, and large language models - facilitate personalized therapy and functional recovery, albeit with heterogeneous evidence and limited prospective validation. AI-based chatbots demonstrate potential to support patient education, mental health interventions, and communication workflows, particularly as adjuncts to clinician-led care. Despite these advances, challenges related to generalizability, algorithmic bias, ethical implementation, and regulatory oversight persist. Overall, this review underscores that AI should be regarded as a supportive clinical decision-support technology rather than a replacement for healthcare professionals, with future research prioritizing prospective validation, real-world effectiveness, and responsible integration into routine clinical practice.
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.