Evidence map›Paper›PMID 38288398›Full record

ReviewBME frontiers2024

Making "CASES" for AI in Medicine.

Ge Wang

Abstract readReview
In one paragraph

Review in BME frontiers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

1 author.

Ge WangBiomedical Imaging Center, Center for Computational Innovations, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.

Funding

Cardiac CT DebloomingR01HL151561 · NHLBI · GENERAL ELECTRIC GLOBAL RESEARCH CTR · PI BUDOFF, MATTHEW J, DE MAN, BRUNO · 2020 to 2023
$3.8M
Photon-counting X-ray and Optical Tomography for Preclinical Cancer ResearchR01CA237267 · NCI · RENSSELAER POLYTECHNIC INSTITUTE · PI BARROSO, MARGARIDA, INTES, XAVIER · 2019 to 2023
$2.9M
Constrained Disentanglement (CODE) Network for CT Metal Artifact Reduction in Radiation TherapyR01EB031102 · NIBIB · RENSSELAER POLYTECHNIC INSTITUTE · PI DE MAN, BRUNO, PAGANETTI, HARALD · 2021 to 2021
$2.6M
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical ImagingR01EB032716 · NIBIB · RENSSELAER POLYTECHNIC INSTITUTE · PI JIA, XUN, MUELLER, KLAUS · 2022 to 2025
$2.5M
NCI NIH HHS R01 CA237267NHLBI NIH HHS R01 HL151561NIBIB NIH HHS R01 EB031102NIBIB NIH HHS R01 EB032716
6 · The paper itself

Abstract

In this perspective, "CASES" are made for AI in medicine. The CASES mean Confidence, Adaptability, Stability, Explainability, and Security of AI systems. We underline that these CASES can be addressed not only individually but also synergistically on the large model platform and using cutting-edge diffusion-type models.

Identifiers

PMID38288398
PMCPMC10823727

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.