Journal article 13 views
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Joseph E Alderman,
Joanne Palmer,
Elinor Laws,
Melissa D McCradden,
Johan Ordish,
Marzyeh Ghassemi,
Stephen R Pfohl,
Negar Rostamzadeh,
Heather Cole-Lewis,
Ben Glocker,
Melanie Calvert,
Tom J Pollard,
Jaspret Gill,
Jacqui Gath,
Adewale Adebajo,
Jude Beng,
Cassandra H Leung,
Stephanie Kuku,
Lesley-Anne Farmer,
Rubeta N Matin,
Bilal A Mateen,
Francis McKay,
Katherine Heller,
Alan Karthikesalingam,
Darren Treanor,
Maxine Mackintosh,
Lauren Oakden-Rayner,
Russell Pearson,
Arjun K Manrai,
Puja Myles,
Judit Kumuthini,
Zoher Kapacee,
Neil J Sebire,
Lama H Nazer,
Jarrel Seah,
Ashley Akbari ,
Lew Berman,
Judy W Gichoya,
Lorenzo Righetto,
Diana Samuel,
William Wasswa,
Maria Charalambides,
Anmol Arora,
Sameer Pujari,
Charlotte Summers,
Elizabeth Sapey,
Sharon Wilkinson,
Vishal Thakker,
Alastair Denniston,
Xiaoxuan Liu
The Lancet Digital Health, Volume: 7, Issue: 1, Pages: e64 - e88
Swansea University Author: Ashley Akbari
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/s2589-7500(24)00224-3
Abstract
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Published in: | The Lancet Digital Health |
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ISSN: | 2589-7500 |
Published: |
Elsevier BV
2025
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Online Access: |
Check full text
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URI: | https://cronfa.swan.ac.uk/Record/cronfa68621 |
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2025-01-02T13:33:24.6085950 v2 68621 2024-12-27 Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 2024-12-27 MEDS Journal Article The Lancet Digital Health 7 1 e64 e88 Elsevier BV 2589-7500 1 1 2025 2025-01-01 10.1016/s2589-7500(24)00224-3 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee STANDING Together was funded by The NHS AI Lab and The Health Foundation and was supported by the National Institute for Health and Care Research (NIHR; AI_HI200014). 2025-01-02T13:33:24.6085950 2024-12-27T19:28:56.2676141 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Joseph E Alderman 1 Joanne Palmer 2 Elinor Laws 3 Melissa D McCradden 4 Johan Ordish 5 Marzyeh Ghassemi 6 Stephen R Pfohl 7 Negar Rostamzadeh 8 Heather Cole-Lewis 9 Ben Glocker 10 Melanie Calvert 11 Tom J Pollard 12 Jaspret Gill 13 Jacqui Gath 14 Adewale Adebajo 15 Jude Beng 16 Cassandra H Leung 17 Stephanie Kuku 18 Lesley-Anne Farmer 19 Rubeta N Matin 20 Bilal A Mateen 21 Francis McKay 22 Katherine Heller 23 Alan Karthikesalingam 24 Darren Treanor 25 Maxine Mackintosh 26 Lauren Oakden-Rayner 27 Russell Pearson 28 Arjun K Manrai 29 Puja Myles 30 Judit Kumuthini 31 Zoher Kapacee 32 Neil J Sebire 33 Lama H Nazer 34 Jarrel Seah 35 Ashley Akbari 0000-0003-0814-0801 36 Lew Berman 37 Judy W Gichoya 38 Lorenzo Righetto 39 Diana Samuel 40 William Wasswa 41 Maria Charalambides 42 Anmol Arora 43 Sameer Pujari 44 Charlotte Summers 45 Elizabeth Sapey 46 Sharon Wilkinson 47 Vishal Thakker 48 Alastair Denniston 49 Xiaoxuan Liu 50 |
title |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
spellingShingle |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations Ashley Akbari |
title_short |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
title_full |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
title_fullStr |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
title_full_unstemmed |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
title_sort |
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations |
author_id_str_mv |
aa1b025ec0243f708bb5eb0a93d6fb52 |
author_id_fullname_str_mv |
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari |
author |
Ashley Akbari |
author2 |
Joseph E Alderman Joanne Palmer Elinor Laws Melissa D McCradden Johan Ordish Marzyeh Ghassemi Stephen R Pfohl Negar Rostamzadeh Heather Cole-Lewis Ben Glocker Melanie Calvert Tom J Pollard Jaspret Gill Jacqui Gath Adewale Adebajo Jude Beng Cassandra H Leung Stephanie Kuku Lesley-Anne Farmer Rubeta N Matin Bilal A Mateen Francis McKay Katherine Heller Alan Karthikesalingam Darren Treanor Maxine Mackintosh Lauren Oakden-Rayner Russell Pearson Arjun K Manrai Puja Myles Judit Kumuthini Zoher Kapacee Neil J Sebire Lama H Nazer Jarrel Seah Ashley Akbari Lew Berman Judy W Gichoya Lorenzo Righetto Diana Samuel William Wasswa Maria Charalambides Anmol Arora Sameer Pujari Charlotte Summers Elizabeth Sapey Sharon Wilkinson Vishal Thakker Alastair Denniston Xiaoxuan Liu |
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The Lancet Digital Health |
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7 |
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e64 |
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2025 |
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Swansea University |
issn |
2589-7500 |
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10.1016/s2589-7500(24)00224-3 |
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Elsevier BV |
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Faculty of Medicine, Health and Life Sciences |
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|
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
department_str |
Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science |
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published_date |
2025-01-01T20:37:02Z |
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