Table 4 ELSIs discussed in the nine alternative perspectives articles
Article no. (ref); title | Short description of ELSIs |
|---|---|
A143 [50]; Medical AI can now predict survival rates—but it’s not ready to unleash on patients | Historical bias—algorithms that use historical data may produce biased outputs (e.g. algorithms may find a relationship between a disease and a minority group that has historically had worse access to healthcare) |
Black box systems—problems arise when doctors cannot access information about the features algorithms use to produce outputs | |
Physician deskilling—doctors may become over-reliant on algorithms to make decisions and lose the skills to make those decisions without the aid of algorithms | |
A22 [46]; Paging Doctor AI: Artificial intelligence promises all sorts of advances for medicine. And all sorts of concerns | Harm to patients—if AI fails to integrate into workflows or is poorly validated for clinical use it may lead to worse patient outcomes |
Value tension between health and for-profit enterprise—AI is proprietary and there is a value collision with the bedside clinician | |
Impact on clinician workflow—AI may be given authority over clinician workflow (e.g. patients’ insurers may only reimburse for the treatments an algorithm recommends, meaning clinicians lose their ability to exercise their own discretion in treating patients) | |
Exacerbation of human bias—when algorithms are not designed to take structural inequalities into account, they will produce flawed results | |
A93 [49]; Genetic Testing Companies Take DNA Tests To A Whole New Level | Concerns about data privacy—using AI tools routinely will raise the need for better data protection regulations |
A91 [47]; From suicide prevention to genetic testing, there's a widening disconnect between Silicon Valley health-tech and outside experts who see red flags | Lacking involvement with medical research—concerns developers of AI are not using normal channels for testing and disseminating algorithms. Claims that they make to consumers are unvalidated and the safety of innovations are not regulated |
Poor transparency protocol in tech companies | |
Value tension between health and for-profit enterprise—tech emphasises disruption and convenience, whereas healthcare emphasises safety. The values behind AI development conflict with the Hippocratic oath | |
Harm to patients—poorly implemented algorithms may lead to iatrogenic health impacts | |
A3 [45]; The AI governance challenge | Need for better data protection regulations |
Value tension between public and for-profit values | |
A113 [51]; How A.I. Can Save Your Life | Concerns about data privacy |
A117 [52]; How tech giants like Google are targeting the seismic NHS data goldmine | Concerns about data privacy—private companies requesting access to public healthcare data |
A8 [53]; Addressing Cyber Security Healthcare and Data Integrity | Concerns about data privacy |
A260 [48]; Vietnam: AI for early warning about liver cancer | Inaccuracy of AI techniques |