Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI...
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Autor*in:	van de Venter, Riaan [verfasserIn] Skelton, Emily Matthew, Jacqueline Woznitza, Nick Tarroni, Giacomo Hirani, Shashivadan P. Kumar, Amrita Malik, Rizwan Malamateniou, Christina

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial intelligence Radiography Education Evaluation Action research

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Insights into imaging - Berlin : Springer, 2010, 14(2023), 1 vom: 03. Feb.
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:1 ; day:03 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s13244-023-01372-2

Katalog-ID:	SPR04924535X

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520			\|a Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
520			\|a Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training.
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spelling	10.1186/s13244-023-01372-2 doi (DE-627)SPR04924535X (SPR)s13244-023-01372-2-e DE-627 ger DE-627 rakwb eng van de Venter, Riaan verfasserin aut Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. Artificial intelligence (dpeaa)DE-He213 Radiography (dpeaa)DE-He213 Education (dpeaa)DE-He213 Evaluation (dpeaa)DE-He213 Action research (dpeaa)DE-He213 Skelton, Emily aut Matthew, Jacqueline aut Woznitza, Nick aut Tarroni, Giacomo aut Hirani, Shashivadan P. aut Kumar, Amrita aut Malik, Rizwan aut Malamateniou, Christina (orcid)0000-0002-2352-8575 aut Enthalten in Insights into imaging Berlin : Springer, 2010 14(2023), 1 vom: 03. Feb. (DE-627)621547425 (DE-600)2543323-4 1869-4101 nnns volume:14 year:2023 number:1 day:03 month:02 https://dx.doi.org/10.1186/s13244-023-01372-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 03 02
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allfieldsGer	10.1186/s13244-023-01372-2 doi (DE-627)SPR04924535X (SPR)s13244-023-01372-2-e DE-627 ger DE-627 rakwb eng van de Venter, Riaan verfasserin aut Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. Artificial intelligence (dpeaa)DE-He213 Radiography (dpeaa)DE-He213 Education (dpeaa)DE-He213 Evaluation (dpeaa)DE-He213 Action research (dpeaa)DE-He213 Skelton, Emily aut Matthew, Jacqueline aut Woznitza, Nick aut Tarroni, Giacomo aut Hirani, Shashivadan P. aut Kumar, Amrita aut Malik, Rizwan aut Malamateniou, Christina (orcid)0000-0002-2352-8575 aut Enthalten in Insights into imaging Berlin : Springer, 2010 14(2023), 1 vom: 03. Feb. (DE-627)621547425 (DE-600)2543323-4 1869-4101 nnns volume:14 year:2023 number:1 day:03 month:02 https://dx.doi.org/10.1186/s13244-023-01372-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 03 02
allfieldsSound	10.1186/s13244-023-01372-2 doi (DE-627)SPR04924535X (SPR)s13244-023-01372-2-e DE-627 ger DE-627 rakwb eng van de Venter, Riaan verfasserin aut Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. Artificial intelligence (dpeaa)DE-He213 Radiography (dpeaa)DE-He213 Education (dpeaa)DE-He213 Evaluation (dpeaa)DE-He213 Action research (dpeaa)DE-He213 Skelton, Emily aut Matthew, Jacqueline aut Woznitza, Nick aut Tarroni, Giacomo aut Hirani, Shashivadan P. aut Kumar, Amrita aut Malik, Rizwan aut Malamateniou, Christina (orcid)0000-0002-2352-8575 aut Enthalten in Insights into imaging Berlin : Springer, 2010 14(2023), 1 vom: 03. Feb. (DE-627)621547425 (DE-600)2543323-4 1869-4101 nnns volume:14 year:2023 number:1 day:03 month:02 https://dx.doi.org/10.1186/s13244-023-01372-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 1 03 02
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author	van de Venter, Riaan
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title_sort	artificial intelligence education for radiographers, an evaluation of a uk postgraduate educational intervention using participatory action research: a pilot study
title_auth	Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study
abstract	Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. © The Author(s) 2023
abstractGer	Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. © The Author(s) 2023
abstract_unstemmed	Background Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training. © The Author(s) 2023
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title_short	Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study
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Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. Methodology A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Results Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. Conclusions The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Key points A novel postgraduate module on AI for radiographers was developed and evaluated.Blended-learning delivery, customisable and contextualised course content, and interprofessional faculty are the ways forward for an ideal AI course for radiographers.Future courses could use this approach to develop their own AI training.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Radiography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Education</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evaluation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Action research</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Skelton, Emily</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Matthew, Jacqueline</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Woznitza, Nick</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tarroni, Giacomo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hirani, Shashivadan P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar, Amrita</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malik, Rizwan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malamateniou, Christina</subfield><subfield code="0">(orcid)0000-0002-2352-8575</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Insights into imaging</subfield><subfield code="d">Berlin : Springer, 2010</subfield><subfield code="g">14(2023), 1 vom: 03. 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Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

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