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- McKay, Francis, et al.
(författare)
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The ethical challenges of artificial intelligence-driven digital pathology
- 2022
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Ingår i: The journal of pathology. Clinical research. - : Wiley. - 2056-4538. ; 8:3, s. 209-216
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Forskningsöversikt (refereegranskat)abstract
- Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new possibilities for health care in recent years, particularly in the opportunities it brings for artificial intelligence (Al)-driven research. Recognising, however, that there is little scholarly debate on the ethics of digital pathology when used for Al research, this paper summarises what it sees as four key ethical issues to consider when deploying Al infrastructures in pathology, namely, privacy, choice, equity, and trust. The themes are inspired from the authors experience grappling with the challenge of deploying an ethical digital pathology infrastructure to support Al research as part of the National Pathology Imaging Cooperative (NPIC), a collaborative of universities, hospital trusts, and industry partners largely located across the North of England. Though focusing on the UK case, internationally, few pathology departments have gone fully digital, and so the themes developed here offer a heuristic for ethical reflection for other departments currently making a similar transition or planning to do so in the future. We conclude by promoting the need for robust public governance mechanisms in Al-driven digital pathology.
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| 2. |
- Nkurunziza, Theoneste, et al.
(författare)
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mHealth-community health worker telemedicine intervention for surgical site infection diagnosis : a prospective study among women delivering via caesarean section in rural Rwanda
- 2022
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Ingår i: BMJ Global Health. - : BMJ Publishing Group. - 2059-7908. ; 7:7
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Tidskriftsartikel (refereegranskat)abstract
- Background Surgical site infections (SSIs) cause a significant global public health burden in low and middle-income countries. Most SSIs develop after patient discharge and may go undetected. We assessed the feasibility and diagnostic accuracy of an mHealth-community health worker (CHW) home-based telemedicine intervention to diagnose SSIs in women who delivered via caesarean section in rural Rwanda. Methods This prospective cohort study included women who underwent a caesarean section at Kirehe District Hospital between September 2019 and March 2020. At postoperative day 10 (+/- 3 days), a trained CHW visited the woman at home, provided wound care and transmitted a photo of the wound to a remote general practitioner (GP) via WhatsApp. The GP reviewed the photo and made an SSI diagnosis. The next day, the woman returned to the hospital for physical examination by an independent GP, whose SSI diagnosis was considered the gold standard for our analysis. We describe the intervention process indicators and report the sensitivity and specificity of the telemedicine-based diagnosis. Results Of 787 women included in the study, 91.4% (n=719) were located at their home by the CHW and all of them (n=719, 100%) accepted the intervention. The full intervention was completed, including receipt of GP telemedicine diagnosis within 1 hour, for 79.0% (n=623). The GPs diagnosed 30 SSIs (4.2%) through telemedicine and 38 SSIs (5.4%) through physical examination. The telemedicine sensitivity was 36.8% and specificity was 97.6%. The negative predictive value was 96.4%. Conclusions Implementation of an mHealth-CHW home-based intervention in rural Rwanda and similar settings is feasible. Patients acceptance of the intervention was key to its success. The telemedicine-based SSI diagnosis had a high negative predictive value but a low sensitivity. Further studies must explore strategies to improve accuracy, such as accompanying wound images with clinical data or developing algorithms using machine learning.
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