Meier et al. describe a pilot study that developed METHAD, an AI-based Medical Ethics Advisor tool that draws on the principlism approach and was tested using text-book cases and clinical et...

In their proof-of-concept, Meier and colleagues describe the purpose and programming decisions underpinning Medical Ethics Advisor, an automated decision support system used t...

Allowing algorithms to guide or determine decision-making in ethically complex situations, and eventually satisfying the need for good clinical ethics consultation work, is a philosophically intere...

The COVID-19 pandemic has been overwhelming public health-care systems around the world. With demand exceeding the availability of medical resources in several regions, hospitals have been forced to invoke triage. To ensure that this difficult task proceeds in a fair and organised manner, governments scrambled experts to draft triage guidelines under enormous time pressure. Although there are similarities between the documents, they vary considerably in how much weight their respective authors place on the different criteria that they propose. Since most (...) of the recommendations do not come with ethical justifications, analysing them requires that one traces back these criteria to their underlying theories of distributive justice. In the literature, COVID-19 triage has been portrayed as a value conflict solely between utilitarian and egalitarian elements. While these two accounts are indeed the main antipodes, I shall show that in fact all four classic theories of distributive justice are involved: utilitarianism, egalitarianism, libertarianism, and communitarianism. Detecting these in the documents and classifying the suggested criteria accordingly enables one to understand the balancing between the different approaches to distributive justice—which is crucial for both managing the current pandemic and in preparation for the next global health crisis. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

In October 2021, the Allen Institute for Artificial Intelligence publicly released Delphi, an artificial intelligence system trained to make general moral decisions (Allen Institute for Artifi...

Machine learning and other forms of artificial intelligence can improve parts of clinical decision making regarding the gathering and analysis of data, the detection of disease, and the provis...

Book reviewed in this article: Principles of Biomedical Ethics. By Tom L. Beauchamp and James F. Childress.

Lukas J. Meier et al. offer the promise of a pathway for resolving clinical bioethical problems using an artificial intelligence interface. The ultimate goal, we assume, is...

In their paper “Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept,” Meier et al. present the design and preliminary results of a proof-of-concept clinical ethics algor...

Answering the design questions inherent to building and deploying machine learning tools —based on algorithms that can learn from and make predictions on large data sets without being explicitl...

On reading “Algorithms for Ethical Decision-Making in the Clinical: A Proof of Concept,” I imagined that for some the fundamental problem with the authors' approach is the very...

Artificial intelligence has found its way into many areas of human life, serving a range of purposes. Sometimes AI tools are designed to help humans eliminate high-volume, tedious, routine tas...

When considering the introduction of AI to support medical decision-making, one must take an end-to-end, holistic approach to development, evaluation, integration and governance. (Cabitza and Zeito...

Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at (...) a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causality was not just a victory for common sense, but a revolution in the study of the world. (shrink)

Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high degree (...) of interpretability. Possible applications of such a system include informal guidance on medical ethics dilemmas as well as educational purposes. (shrink)