Evidence map›Paper›PMID 37887265›Full record

ReviewMedicines (Basel, Switzerland)2023

Exploring the Potential of Chatbots in Critical Care Nephrology.

Supawadee Suppadungsuk, Charat Thongprayoon, Jing Miao, Pajaree Krisanapan, Fawad Qureshi, Kianoush Kashani, Wisit Cheungpasitporn

Abstract readReview
In one paragraph

Review in Medicines (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026
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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

7 authors.

Supawadee SuppadungsukDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-1597-2411
Charat ThongprayoonDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Jing MiaoDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-0642-9740
Pajaree KrisanapanDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-2888-881X
Fawad QureshiDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Kianoush KashaniDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-2184-3683
Wisit CheungpasitpornDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-9954-9711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

Indexed as

artificial intelligencechatbotscritical care nephrologynephrology

Identifiers

PMID37887265
PMCPMC10608511

What Socratic 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 Socratic graph.