Evidence map›Paper›PMID 40291306›Full record

ArticleCureus2025

Inspired Spine Smart Universal Resource Identifier (SURI): An Adaptive AI Framework for Transforming Multilingual Speech Into Structured Medical Reports.

Jiawen Zhan, Dominic Moore, Yuanzhe Lu, Hamid Abbasi

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiawen ZhanMachine Learning, Inspired Spine Health, Burnsville, USA.
Dominic MooreSpine Surgery, Inspired Spine Health, Burnsville, USA.
Yuanzhe LuApplied AI and Programming, Avicenna Technical University (ATU), Burnsville, USA.
Hamid AbbasiSpine Surgery, Avicenna Technical University (ATU) and Inspired Spine Health, Burnsville, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical documentation is a major part of delivering healthcare worldwide and is gaining more importance in developing countries as well. The global spread of multilingual communities in medical documentation poses unique challenges, particularly regarding maintaining accuracy and consistency across diverse languages. Inspired Spine Smart Universal Resource Identifier (SURI), an adaptive artificial intelligence (AI) framework, addresses these challenges by transforming multilingual speech into structured medical reports. Utilizing state-of-the-art automatic speech recognition (ASR) and natural language processing (NLP) technologies, SURI converts doctor-patient dialogues into detailed clinical documentation. This paper presents SURI's development, focusing on its multilingual capabilities, effective report generation, and continuous improvement through real-time feedback. Our evaluation indicates a 60% reduction in documentation errors and a 70% decrease in time spent on medical reporting compared to traditional methods. SURI not only provides a practical solution to a pressing issue in healthcare but also sets a benchmark for integrating AI into medical communication workflows.

Indexed as

artificial intelligence in medicinedictation softwarelanguage interpretation serviceslarge language model(llm)multilingualpatient encounter

Identifiers

PMID40291306
PMCPMC12029695

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.