Table 3.

AI applications for clinical ethical decision-making as occurring in the sample

Name	Technological Basis	Field of Application	Use	Implemented/Conjectured
Medical Ethical Advisor (METHAD) [17]	Machine Learning, Fuzzy Cognitive Maps	General clinical practice, education	- encompasses the bioethical principles (Beauchamp, Childress) [20] in machine-readable form - input: patient status and preferences in machine-comparable values - general evaluation; numerical value of zero (against) to one (in favour of)	Implemented (“proof of concept”)
Patient Preference Predictor (PPP) [23]	Machine Learning, Population-based	General clinical practice, incapacitated patients	- takes defining characteristics and circumstances of the patient in question and empirical data on treatment preferences into account - approximates preferences of incapacitated patients regarding treatments	Conjectured
Do not attempt resuscitation—Algorithm (DNAR) [24]	Machine Learning	Emergency medicine	- predicts patients’ preferences on resuscitation measures in emergency situations - compares the patient’s data with that of other patients	Conjectured
Surgery Algorithm [25]	Machine Learning	Surgery	- strives to de-bias decision-making in the selection of patients for major surgery - gives an objective and equitable risk assessment for the patients - improves i.a. racial and socioeconomic justice	Conjectured
Autonomy Algorithm [26]	Machine Learning, based on healthcare records and social media	General clinical practice, incapacitated patients	- harvests information on patients with impaired capacity - predicts their preferences on important healthcare decisions	Conjectured