ReviewCureus2025
The Evolution of Automated Medical Billing With Artificial Intelligence: A Review With a Global and Saudi Perspective.
Review in Cureus, 2025. 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.
- Cyberspace Supplementary Health Services and Patient Loyalty: The Role of Patient Experience in Saudi Arabia.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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical billing is a complex and evolving aspect of healthcare administration that requires accurate documentation and efficient reimbursement processes to maintain institutional financial sustainability. This review explores how artificial intelligence (AI) is transforming medical billing through automation, error reduction, and fraud detection, with emphasis on global developments and initiatives in Saudi Arabia. A comprehensive review of recent studies and real-world applications was conducted to assess AI-driven innovations in automated coding, claims processing, and financial management. AI-based platforms have demonstrated substantial reductions in coding errors and faster claim turnaround times. AI algorithms trained on large billing datasets identify discrepancies in submitted claims, detect potential fraud, and recommend corrective actions, enhancing transparency and compliance. Automated coding systems translate clinical documentation into standardized billing terminology, a global coding framework that assigns numeric identifiers to medical procedures and services, thus improving consistency and reducing human error. Despite these advancements, challenges remain, including data privacy, algorithmic bias, and infrastructure limitations. In conclusion, integrating AI into medical billing can revolutionize administrative efficiency, reduce operational costs, and promote financial sustainability across healthcare systems worldwide, including Saudi Arabia.
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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.