Machine Learning for Healthcare, 2022


Poster about STANDING together which can be accessed via the link on this page

Click here to view our poster presented at the 2022 Machine Learning for Healthcare (MLHC) conference.


Poster reference list

  1. Artificial Intelligence - how to get it right. NHSX. October 2019. Available from URL: https://transform.england.nhs.uk/media/documents/NHSX_AI_report.pdf

  2. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. 2019 Oct 1;1(6):e271–97. Available online at: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(19)30123-2/fulltext

  3. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019 Oct 25;366(6464):447–53. Available online at: https://www.science.org/doi/10.1126/science.aax2342

  4. Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021 Dec;27(12):2176–82. Available online at: https://www.nature.com/articles/s41591-021-01595-01.

  5. The health of people from ethnic minority groups in England [Internet]. The King’s Fund. 2021 [cited 2022 Apr 12]. Available from: https://www.kingsfund.org.uk/publications/health-people-ethnic-minority-groups-england1.

  6. Khan SM, Liu X, Nath S, Korot E, Faes L, Wagner SK, et al. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability. The Lancet Digital Health. 2021 Jan 1;3(1):e51–66. Available online at: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30240-5/fulltext1.

  7. Wen D, Khan SM, Xu AJ, Ibrahim H, Smith L, Caballero J, et al. Characteristics of publicly available skin cancer image datasets: a systematic review. The Lancet Digital Health. 2022 Jan 1;4(1):e64–74. Available online at: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00252-1/fulltext1.

  8. Ibrahim H, Liu X, Zariffa N, Morris AD, Denniston AK. Health data poverty: an assailable barrier to equitable digital health care. The Lancet Digital Health. 2021 Apr 1;3(4):e260–5. Available online at: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30317-4/fulltext1.

  9. Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA. 2019 Dec 24;322(24):2377–8. Available online at: https://jamanetwork.com/journals/jama/article-abstract/2756196 1.

  10. Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical Machine Learning in Healthcare. Annu Rev Biomed Data Sci. 2021 Jul;4:123–44. Available online at: https://www.annualreviews.org/doi/10.1146/annurev-biodatasci-092820-114757 1.

  11. Bennett JE, Pearson-Stuttard J, Kontis V, Capewell S, Wolfe I, Ezzati M. Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: a population-based analysis of vital registration data. The Lancet Public Health. 2018 Dec 1;3(12):e586–97. Available online at: https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30214-7/fulltext 1.

  12. Marmot M, Allen J, Boyce T, Goldblatt P, Morrison J. Health Equity in England: The Marmot Review 10 Years On - The Health Foundation [Internet]. London: Institute of Health Equity; 2020 [cited 2022 Apr 13]. Available online at: https://www.health.org.uk/publications/reports/the-marmot-review-10-years-on