Evidence map›Paper›PMID 37723300›Full record

ReviewCurrent urology reports2024

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence.

Ali Talyshinskii, Nithesh Naik, B M Zeeshan Hameed, Patrick Juliebø-Jones, Bhaskar Kumar Somani

Abstract readReview
PubMed Full text at PMC 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 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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  7. Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026
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  19. The Frontiers of Smart Healthcare Systems.Healthcare (Basel, Switzerland) · 2024
    Review
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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

5 authors.

Ali TalyshinskiiDepartment of Urology, Astana Medical University, Astana, Kazakhstan.
Nithesh NaikDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
B M Zeeshan HameedDepartment of Urology, Father Muller Medical College, Mangalore, Karnataka, India.
Patrick Juliebø-JonesDepartment of Urology, Haukeland University Hospital, Bergen, Norway. jonesurology@gmail.com.
Bhaskar Kumar SomaniDepartment of Urology, University Hospital Southampton, Southampton, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewArtificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today. RECENT

findingsThe capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.

Indexed as

PhysiciansUrologyArtificial IntelligenceHumansPatient CareArtificial intelligenceChatbotGPTHealthcareUrology

Identifiers

PMID37723300
PMCPMC10787686

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.