Imperial College London
Alternate

Professor the Lord Darzi of Denham PC KBE FRS FMedSci HonFREng

Faculty of Medicine, Department of Surgery & Cancer

Co-Director of the IGHI, Professor of Surgery
 
 
 
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Contact

 

+44 (0)20 3312 1310a.darzi

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@inbook{Sounderajah:2022:10.1007/978-3-030-64573-1_34,
author = {Sounderajah, V and Normahani, P and Aggarwal, R and Jayakumar, S and Markar, SR and Ashrafian, H and Darzi, A},
booktitle = {Artificial Intelligence in Medicine},
doi = {10.1007/978-3-030-64573-1_34},
pages = {385--395},
title = {Reporting Standards and Quality Assessment Tools in Artificial Intelligence-Centered Healthcare Research},
url = {http://dx.doi.org/10.1007/978-3-030-64573-1_34},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - The practice of incomplete study reporting is rife within scientific literature. It hinders the adoption of technologies, introduces considerable “research waste, " and represents a significant moral hazard. In order to combat this issue, there has been a shift towards the use of reporting standards and quality assessment tools, a move that has been endorsed by major biomedical journals as well as other key stakeholders. These instruments help [1] to improve the quality and completeness of study reporting as well as [2] to aid researchers in their assessment of a study’s risk of bias and applicability. These instruments are carefully created through a multistep evidence generation process and are specific to individual study designs or specialties. Recently, it has been noted that many of the existing instruments are poorly suited to aid the reporting and assessment of artificial intelligence (AI)- based studies on account of their niche study considerations. As such, there has been a concerted effort to produce AI-specific extensions to preexisting instruments, such as CONSORT, SPIRIT, STARD, TRIPOD, QUADAS, and PROBAST. This chapter expands upon why AI-specific amendments to these instruments are required in addition to highlighting their contents and proposed scope.
AU  - Sounderajah,V
AU  - Normahani,P
AU  - Aggarwal,R
AU  - Jayakumar,S
AU  - Markar,SR
AU  - Ashrafian,H
AU  - Darzi,A
DO  - 10.1007/978-3-030-64573-1_34
EP  - 395
PY  - 2022///
SN  - 9783030645724
SP  - 385
TI  - Reporting Standards and Quality Assessment Tools in Artificial Intelligence-Centered Healthcare Research
T1  - Artificial Intelligence in Medicine
UR  - http://dx.doi.org/10.1007/978-3-030-64573-1_34
ER  -
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