Evidence map›Paper›PMID 39725798›Full record

ReviewCurrent urology reports2024

Artificial Intelligence (AI) and Men's Health Clinic Efficiency and Clinic Billing.

Nickolas Kinachtchouk, David Canes

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current urology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

2 authors.

Nickolas KinachtchoukDepartment of Urology, Lahey Hospital and Medical Center, MA, Burlington, USA. nickolas.kinachtchouk@lahey.org.ORCID https://orcid.org/0000-0001-8518-1506
David CanesDepartment of Urology, Lahey Hospital and Medical Center, MA, Burlington, USA.ORCID http://orcid.org/0000-0001-6280-2285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewArtificial Intelligence (AI) has produced a significant impact across various industries, including healthcare. In the outpatient clinic setting, AI offers promising improvements in efficiency through Chatbots, streamlined medical documentation, and personalized patient education materials. On the billing side, AI technologies hold potential for optimizing the selection of appropriate billing codes, automating prior authorizations, and enhancing healthcare fraud detection. The purpose of this review is to explore the current applications of AI in men's health clinics, with a focus on enhancing clinic efficiency and billing practices. RECENT

findingsCurrent uses of AI, including AI-powered Chatbots, Large Language Models (LLM) and Natural Language Processing (NLP), are discussed with a focus on their application in men's health clinics. Additionally, the challenges associated with their implementation are highlighted.

Indexed as

Ambulatory Care FacilitiesArtificial IntelligenceMen's HealthEfficiency, OrganizationalHumansMaleNatural Language ProcessingArtificial intelligenceBillingEfficiencyMen’s healthNatural language processing

Identifiers

PMID39725798

What OpenQuestion holds

Textmetadata
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.