ArticleInfectious diseases of poverty2025
Artificial intelligence for healthcare: restrained development despite impressive applications.
Article in Infectious diseases of poverty, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.NPJ digital medicine · 2026Article
- Review
- A Multiphase CT-Based Integrated Deep Learning Framework for Rectal Cancer Detection, Segmentation, and Staging: Performance Comparison with Radiologist Assessment.Journal of imaging · 2026Article
- Topic-Modeling Guided Semantic Clustering for Enhancing CNN-Based Image Classification Using Scale-Invariant Feature Transform and Block Gabor Filtering.Journal of imaging · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) remains poorly understood and its rapid growth raises concerns reminiscent of dystopian narratives. AI has shown the capability of producing new medical content and improving management through optimization and standardization, which shortens queues, while its complete reliance on technical solutions threatens the traditional doctor-patient bond. APPROACH: Based on the World Economic Forum's emphasis on the need for faster AI adoption in the medical field, we highlight current gaps in the understanding of its application and offer a set of priorities for future research. The historic review of AI and the latest publications point at barriers like complexity and fragmented regulations, while assisted analysis of big data offers new insights. AI's potential in healthcare is linked to the breakthrough from rule-based computing, enabling autonomy through learning from experience and the capacity of reasoning. Without AI, protein folding would have remained unsolved, as emphasized by the Nobel-honored AlphaFold2 approach. It is expected that AI's role in diagnostics, disease control, geospatial health and epidemiology will lead to similar progress.
conclusionsAI boosts efficiency, drives innovation, and solves complex problems but can also deepen biases and create security threats. Controlled progress requires industry collaboration leading to prompt acceleration of proper incorporation of AI into the health sphere. Cooperation between governments as well as both public and private sectors with a multi-actor approach is needed to effectively address these challenges. To fully harness AI's potential in accelerating healthcare reform and shorten queues, while maintaining the compassionate essence of healthcare, a well-coordinated approach involving all stakeholders is necessary.
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.