Are Agency and Responsibility Still Solely Ascribable to Humans? The Case of Medical Decision Support Systems

2011 ◽  
pp. 84-112
Author(s):  
Hannah H. Gröndahl
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Moral Agency ◽  
Support Systems ◽  
Legal Responsibility ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems ◽  
And Function ◽  
Advanced Computer

Are agency and responsibility solely ascribable to humans? This chapter explores the question from legal and ethical perspectives. In addition to presenting important theories, the chapter uses arguments, counterarguments, and scenarios to clarify both the actual and the hypothetical ethical and legal situations governing a very particular type of advanced computer system: medical decision support systems (MDSS) that feature AI in their system design. The author argues that today’s MDSS must be categorized by more than just type and function even to begin ascribing some level of moral or legal responsibility. As the scenarios demonstrate, various U.S. and UK legal doctrines appear to allow for the possibility of assigning specific types of agency—and thus specific types of legal responsibility—to some types of MDSS. The author concludes that strong arguments for assigning moral agency and responsibility are still lacking, however.

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 251-262
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 1068-1079
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

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