scholarly journals Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

2021 ◽  
pp. medethics-2021-107440
Author(s):  
Hendrik Kempt ◽  
Saskia K Nagel
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Ethical Issue ◽  
Support Systems ◽  
Second Opinion ◽  
Clinical Context ◽  
Peer Disagreement ◽  
Responsibility Attribution ◽  
Artificial Intelligent ◽  
Different Types

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments.

Prioritization of IT Product Backlog Items Using Decision Support Systems

2020 ◽  
Vol 26 (11) ◽  
pp. 631-640
Author(s):  
T. K. Kravchenko ◽  
◽  
S. N. Bruskin ◽  
D. V. Isaev ◽  
E. V. Kuznetsova ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Group Decision ◽  
Support Systems ◽  
Problem Situations ◽  
User Stories ◽  
Different Types ◽  
High Level ◽  
Support Methods

The article focuses on the application of decision support systems for prioritization of product backlog items in IT projects implemented using the Scrum methodology. The study identified the features of prioritization of different types of the product backlog items — user stories, epics and themes. It is justified that high-level product backlog items (epics and themes) require comprehensive prioritization, due to the following reasons. First, high-level product backlog items are particularly important because they determine the planning and implementation of detailed user stories within individual sprints. Second, any high-level item can be considered in terms of different criteria. Third, the implementation of epics and themes takes longer time compared to the implementation of user stories, so it is necessary to take into account possible future states of the project's environment. Fourth, prioritizing epics and themes requires increased objectivity and validity, so group decision making with participation of several experts seems reasonable. Taking into consideration the aforementioned features the conclusion regarding limitations of existing methods of prioritization is made. It is argued that prioritization of high-level product backlog items (epics and themes) may be performed using multi-criteria decision making methods with availability of several problem situations (possible future states of the environment), as well as involvement of several experts. The idea of applying decision support methods and systems is illustrated on the appropriate example. It is also argued that increased consumption of time and resources related with setting and solving decision support tasks may be considered as acceptable for high-level product backlog items.

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

2015 ◽  
Vol 11 (2) ◽  
pp. e206-e211 ◽  
Author(s):  
Peter Paul Yu
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Rapid Learning ◽  
Types Of Knowledge ◽  
Different Types

This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into clinical decision support systems.

Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology

2019 ◽  
Vol 127 ◽  
pp. 18-26 ◽  
Author(s):  
Habibollah Pirnejad ◽  
Parasto Amiri ◽  
Zahra Niazkhani ◽  
Afshin Shiva ◽  
Khadijeh Makhdoomi ◽  
...  
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Decision Support Systems ◽  
Support Systems ◽  
Potential Drug ◽  
Clinical Context

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Sign in / Sign up

Export Citation Format

Share Document