Academic Journal
Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography
| Τίτλος: | Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography |
|---|---|
| Συγγραφείς: | York, T, Jenney, H, Jones, G |
| Στοιχεία εκδότη: | BMJ Publishing Group |
| Έτος έκδοσης: | 2020 |
| Συλλογή: | Imperial College London: Spiral |
| Θεματικοί όροι: | BMJ Health Informatics, computer methodologies, health care, information science, patient care |
| Περιγραφή: | Background Up to half of all musculoskeletal injuries are investigated with plain radiographs. However, high rates of image interpretation error mean that novel solutions such as artificial intelligence (AI) are being explored. Objectives To determine patient confidence in clinician-led radiograph interpretation, the perception of AI-assisted interpretation and management, and to identify factors which might influence these views. Methods A novel questionnaire was distributed to patients attending fracture clinic in a large inner-city teaching hospital. Categorical and Likert scale questions were used to assess participant demographics, daily electronics use, pain score and perceptions towards AI used to assist in interpretation of their radiographs, and guide management. Results 216 questionnaires were included (M=126, F=90). Significantly higher confidence in clinician rather than AI-assisted interpretation was observed (clinician=9.20, SD=1.27 vs AI=7.06, SD=2.13), 95.4% reported favouring clinician over AI-performed interpretation in the event of disagreement. Small positive correlations were observed between younger age/educational achievement and confidence in AI-assistance. Students demonstrated similarly increased confidence (8.43, SD 1.80), and were over-represented in the minority who indicated a preference for AI-assessment over their clinicians (50%). Conclusions Participant’s held the clinician’s assessment in the highest regard and expressed a clear preference for it over the hypothetical AI assessment. However, robust confidence scores for the role of AI-assistance in interpreting skeletal imaging suggest patients view the technology favourably. Findings indicate that younger, more educated patients are potentially more comfortable with a role for AI-assistance however further research is needed to overcome the small number of responses on which these observations are based. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | unknown |
| Relation: | BMJ Health & Care Informatics; http://hdl.handle.net/10044/1/84838 |
| DOI: | 10.1136/bmjhci-2020-100233 |
| Διαθεσιμότητα: | http://hdl.handle.net/10044/1/84838 https://doi.org/10.1136/bmjhci-2020-100233 |
| Rights: | © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. ; http://creativecommons.org/licenses/by-nc/4.0/ |
| Αριθμός Καταχώρησης: | edsbas.8D6710A9 |
| Βάση Δεδομένων: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22York%2C+T%22">York, T</searchLink><br /><searchLink fieldCode="AR" term="%22Jenney%2C+H%22">Jenney, H</searchLink><br /><searchLink fieldCode="AR" term="%22Jones%2C+G%22">Jones, G</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: BMJ Publishing Group – Name: DatePubCY Label: Publication Year Group: Date Data: 2020 – Name: Subset Label: Collection Group: HoldingsInfo Data: Imperial College London: Spiral – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22BMJ+Health+Informatics%22">BMJ Health Informatics</searchLink><br /><searchLink fieldCode="DE" term="%22computer+methodologies%22">computer methodologies</searchLink><br /><searchLink fieldCode="DE" term="%22health+care%22">health care</searchLink><br /><searchLink fieldCode="DE" term="%22information+science%22">information science</searchLink><br /><searchLink fieldCode="DE" term="%22patient+care%22">patient care</searchLink> – Name: Abstract Label: Description Group: Ab Data: Background Up to half of all musculoskeletal injuries are investigated with plain radiographs. However, high rates of image interpretation error mean that novel solutions such as artificial intelligence (AI) are being explored. Objectives To determine patient confidence in clinician-led radiograph interpretation, the perception of AI-assisted interpretation and management, and to identify factors which might influence these views. Methods A novel questionnaire was distributed to patients attending fracture clinic in a large inner-city teaching hospital. Categorical and Likert scale questions were used to assess participant demographics, daily electronics use, pain score and perceptions towards AI used to assist in interpretation of their radiographs, and guide management. Results 216 questionnaires were included (M=126, F=90). Significantly higher confidence in clinician rather than AI-assisted interpretation was observed (clinician=9.20, SD=1.27 vs AI=7.06, SD=2.13), 95.4% reported favouring clinician over AI-performed interpretation in the event of disagreement. Small positive correlations were observed between younger age/educational achievement and confidence in AI-assistance. Students demonstrated similarly increased confidence (8.43, SD 1.80), and were over-represented in the minority who indicated a preference for AI-assessment over their clinicians (50%). Conclusions Participant’s held the clinician’s assessment in the highest regard and expressed a clear preference for it over the hypothetical AI assessment. However, robust confidence scores for the role of AI-assistance in interpreting skeletal imaging suggest patients view the technology favourably. Findings indicate that younger, more educated patients are potentially more comfortable with a role for AI-assistance however further research is needed to overcome the small number of responses on which these observations are based. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: unknown – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: BMJ Health & Care Informatics; http://hdl.handle.net/10044/1/84838 – Name: DOI Label: DOI Group: ID Data: 10.1136/bmjhci-2020-100233 – Name: URL Label: Availability Group: URL Data: http://hdl.handle.net/10044/1/84838<br />https://doi.org/10.1136/bmjhci-2020-100233 – Name: Copyright Label: Rights Group: Cpyrght Data: © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. ; http://creativecommons.org/licenses/by-nc/4.0/ – Name: AN Label: Accession Number Group: ID Data: edsbas.8D6710A9 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1136/bmjhci-2020-100233 Languages: – Text: unknown Subjects: – SubjectFull: BMJ Health Informatics Type: general – SubjectFull: computer methodologies Type: general – SubjectFull: health care Type: general – SubjectFull: information science Type: general – SubjectFull: patient care Type: general Titles: – TitleFull: Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: York, T – PersonEntity: Name: NameFull: Jenney, H – PersonEntity: Name: NameFull: Jones, G IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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