Evidence map›Paper›PMID 35808361›Full record

ArticleSensors (Basel, Switzerland)2022

Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and Application.

Vyacheslav Kharchenko, Herman Fesenko, Oleg Illiashenko

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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. Article
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

3 authors.

Vyacheslav KharchenkoDepartment of Computer Systems, Networks and Cybersecurity, National Aerospace University "KhAI", 17, Chkalov Str., 61070 Kharkiv, Ukraine.ORCID 0000-0001-5352-077X
Herman FesenkoDepartment of Computer Systems, Networks and Cybersecurity, National Aerospace University "KhAI", 17, Chkalov Str., 61070 Kharkiv, Ukraine.ORCID 0000-0002-4084-2101
Oleg IlliashenkoDepartment of Computer Systems, Networks and Cybersecurity, National Aerospace University "KhAI", 17, Chkalov Str., 61070 Kharkiv, Ukraine.ORCID 0000-0002-4672-6400

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.

Indexed as

Artificial Intelligenceartificial intelligencecharacteristicqualityquality model

Identifiers

PMID35808361
PMCPMC9269736

What OpenQuestion holds

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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.