Evidence map›Paper›PMID 38196551›Full record

ArticleProstate international2023

Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea.

Taeyoung Park, Philip Gu, Chang-Hee Kim, Kwang Taek Kim, Kyung Jin Chung, Tea Beom Kim, Han Jung, Sang Jin Yoon, Jin Kyu Oh

Abstract read
In one paragraph

Article in Prostate international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

  1. Pooled it
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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

9 authors.

Taeyoung ParkDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Philip GuDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Chang-Hee KimDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Kwang Taek KimDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Kyung Jin ChungDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Tea Beom KimDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Han JungDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Sang Jin YoonDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.
Jin Kyu OhDepartment of Urology, Gachon University Gil Medical Center, Incheon, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is changing our life, including the medical field. Repeated machine learning using big data made various fields more predictable and accurate. In medicine, IBM Watson for Oncology (WFO), trained by Memorial Slone Kettering Cancer Center (MSKCC), was first introduced and applied in 14 countries worldwide.Our study was designed to assess the feasibility of WFO in actual clinical practice. We aimed to investigate the concordance rate between WFO and multidisciplinary tumor board (MTB) in Urologic cancer patients. Materials and methods: We reviewed retrospectively collected data for consecutive patients who underwent WFO and MTB simultaneously in the diagnosis of urologic malignancy before determining further treatment between August 2017 and September 2020. We compared the recommendation of the AI system, WFO (IBM Watson Health, Cambridge, MA), with the opinion of MTB for further managing all patients diagnosed with urologic malignancies such as prostate, bladder, and kidney cancer. Results: A total of 55 patients were enrolled in our study. The number of patients with prostate cancer was 48. The number of bladder and kidney cancer patients was 5 and 2, respectively. The overall concordance rate between WFO and MTB was 92.7%. Three patients could not suggest proper treatment options using WFO, and the recommended choice of WFO was not feasible in the Korean Health Insurance Review and Assessment Service. Conclusions: The decision of WFO showed a high concordance rate with a multidisciplinary tumor board for urologic oncology. However, some recommendations of WFO were not feasible in actual practice, and WFO still has some points to improve and modify. Interestingly, applying WFO is likely to facilitate a multidisciplinary team approach.

Indexed as

Artificial intelligenceInterdisciplinary communicationUrologic neoplasms

Identifiers

PMID38196551
PMCPMC10772151

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.