Using AI ethically to tackle covid-19

Abstract

Taking a principled approach is crucial to the successful use of AI in pandemic management, say Stephen Cave and colleagues Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare. Provenance and peer review: Commissioned; externally peer reviewed. This collection of articles was proposed by the WHO Department of Digital Health and Innovation and commissioned by The BMJ. The BMJ retained full editorial control over external peer review, editing, and publication of these articles. Open access fees were funded by WHO.

From General Principles to Procedural Values: Responsible Digital Health Meets Public Health Ethics

“…Some governments wanted to base these on a centralized data collection approach, arguing that such datasets could also be used to produce new knowledge to help combat the pandemic. This was resisted by legal and information security experts concerned about potential privacy breaches (36)(37)(38). Appealing to general principles is unlikely to resolve this debate.…”

“…However, pure majority rule is not plausible either. Certain groups and communities may have good reasons, e.g., to value privacy because of their historical experiences of surveillance and discrimination ( 37 ). For instance, during the 1980's AIDS crisis, gay community-based activists initially resisted name-based reporting of infections, arguing that homophobia and AIDS-hysteria made privacy breaches and discrimination against people identified as HIV-positive more likely than for other diseases ( 41 ).…”

Eerste stappen in Europese regulering van artificiële intelligentie: algoritmes en patiëntenrechten

“…68 In het kader van patiëntenrechten doet dit de vraag rijzen in hoeverre de Commissie AI-systemen voor volksgezondheid wil reguleren, zeker nu sinds de COVID-19-pandemie meer AI-toepassingen worden ingezet ter bescherming van de publieke gezondheid en veiligheid. 69 Ook gaat de AIA voorbij aan de tekortkomingen van de AVG voor algoritmische beslissingen waarbij wél een arts betrokken is en beslissingen die betrokkenen niet 'in aanmerkelijke mate treffen'. Daarnaast heeft het wetsvoorstel geen aandacht voor de bredere problematiek omtrent privacy: de tendens om zo veel mogelijk persoonsgegevens te verzamelen schuurt met de doelstelling van de AVG en kan juist patiënten benadelen.…”

Direct detection of humoral marker corelates of COVID-19, glycated HSA and hyperglycosylated IgG3, by MALDI-ToF mass spectrometry

“…identifying the "at risk" from medical notes and pre-existing test results via algorithms trained through machine learning [8,9,10]. However, whether this will be sufficient to give the level of, sensitivity and specificity, "precision" required in identifying all that are at risk is questionable.…”

“…A range of medical technologies are being explored and developed to help inform this targeted or precision medicine approach. Currently, the focus has been on artificial intelligence (AI) identifying the “at risk” from medical notes and pre-existing test results via algorithms trained through machine learning [8,9,10]. However, whether this will be sufficient to give the level of, sensitivity and specificity, “precision” required in identifying all that are at risk is questionable.…”