Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

Artificial Intelligence, Human Intelligence, and Orientism Management Framework

2021 ◽  
pp. 1-30
Author(s):  
Luisa Dall'Acqua
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Makers ◽  
Human Intelligence ◽  
Decision Making Process ◽  
Knowledge Process ◽  
Management Framework ◽  
Theoretical Contribution ◽  
Practical Guideline

The chapter intends to be a theoretical contribution for developers in the field of artificial intelligence. It also means a practical guideline for leaders, as decision-makers, to manage tasks and optimize performance. The proposed approach interprets the fluid nature of the decision-making process looking at knowledge and knowledge activities as dynamic, adaptive, and self-regulative, based not only on well-known explicit curricular goals but also on unpredictable interactions and relationships between players. The knowledge process is emerging in human and biological, social, and cultural environments.

scholarly journals IDENTIFYING KEY FACTORS FOR ADOPTING ARTIFICIAL INTELLIGENCE-ENABLED AUDITING TECHNIQUES BY JOINT UTILIZATION OF FUZZY-ROUGH SET THEORY AND MRDM TECHNIQUE

2020 ◽  
Vol 0 (0) ◽  
pp. 1-34
Author(s):  
Kuang-Hua Hu ◽  
Fu-Hsiang Chen ◽  
Ming-Fu Hsu ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Audit Quality ◽  
Rough Set Theory ◽  
Decision Makers ◽  
Fuzzy Rough Set ◽  
Rule Based ◽  
Cpa Firms

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

scholarly journals The role of non-medical factors in physicians’ decision-making process in a pediatric telemedicine service

2019 ◽  
Vol 26 (2) ◽  
pp. 1152-1176 ◽  
Author(s):  
Motti Haimi ◽  
Shuli Brammli-Greenberg ◽  
Yehezkel Waisman ◽  
Nili Stein ◽  
Orna Baron-Epel
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Decision Making Process ◽  
Medical Decisions ◽  
Telemedicine Service ◽  
Medical Factors ◽  
Complex Process ◽  
Clinical Scenarios

The complex process of medical decision-making is prone also to medically extraneous influences or “non-medical” factors. We aimed to investigate the possible role of non-medical factors in doctors’ decision-making process in a telemedicine setting. Interviews with 15 physicians who work in a pediatric telemedicine service were conducted. Those included a qualitative section, in which the physicians were asked about the role of non-medical factors in their decisions. Their responses to three clinical scenarios were also analyzed. In an additional quantitative section, a random sample of 339 parent -physician consultations, held during 2014–2017, was analyzed retrospectively. Various non-medical factors were identified with respect to their possible effect on primary and secondary decisions, the accuracy of diagnosis, and “reasonability” of the decisions. Various non-medical factors were found to influence physicians’ decisions. Those factors were related to the child, the applying parent, the physician, the interaction between the doctor and parents, the shift, and to demographic considerations, and were also found to influence the ability to make an accurate diagnosis and “reasonable” decisions. Our conclusion was that non-medical factors have an impact on doctor’s decisions, even in the setting of telemedicine, and should be considered for improving medical decisions in this milieu.

scholarly journals Environmental Justice in India: The National Green Tribunal and Expert Members

2015 ◽  
Vol 5 (1) ◽  
pp. 175-205 ◽  
Author(s):  
Gitanjali Nain Gill
Keyword(s):  
Decision Making ◽  
Environmental Justice ◽  
Decision Makers ◽  
Decision Making Process ◽  
Environmental Disputes ◽  
Scientific Experts ◽  
Decision Making Processes ◽  
Technical Expert ◽  
Insight Into

AbstractThis article argues that the involvement of technical experts in decision making promotes better environmental results while simultaneously recognizing the uncertainty in science. India’s record as a progressive jurisdiction in environmental matters through its proactive judiciary is internationally recognized. The neoteric National Green Tribunal of India (NGT) – officially described as a ‘specialised body equipped with necessary expertise to handle environmental disputes involving multi-disciplinary issues’ – is a forum which offers greater plurality for environmental justice. The NGT, in exercising wide powers, is staffed by judicial and technical expert members who decide cases in an open forum. The experts are ‘central’, rather than ‘marginal’, to the NGT’s decision-making process.This article draws on theoretical insights developed by Lorna Schrefler and Peter Haas to analyze the role of scientific experts as decision makers within the NGT. Unprecedented interview access provides data that grants an insight into the internal decision-making processes of the five benches of the NGT. Reported cases, supported by additional comments of bench members, illustrate the wider policy impact of scientific knowledge and its contribution to the NGT’s decision-making process.

scholarly journals Artificial Intelligence in Cardiac Imaging

2020 ◽  
Vol 13 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Karthik Seetharam ◽  
Sirish Shrestha ◽  
Partho P Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Cardiac Imaging ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Precision Phenotyping ◽  
Cardiac Disorders ◽  
Enhance Patient Care

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

Prioritization of congenital cardiac surgical patients using fuzzy reasoning – a solution to the problem of the waiting list?

2006 ◽  
Vol 16 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Ralf Holzer ◽  
Ed Ladusans ◽  
Denise Kitchiner ◽  
Ian Peart ◽  
Gordon Gladman ◽  
...  
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Production System ◽  
Medical Decision Making ◽  
Waiting List ◽  
Surgical Patients ◽  
Medical Decision ◽  
Impact Factors ◽  
Decision Making Process ◽  
Rule Based

Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent.The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system (“PrioHeart”).The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate “timing category” within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category.We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base.The “PrioHeart” expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of “PrioHeart” on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using “PrioHeart” and those results obtained by the individual consultant specialist.We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.

scholarly journals Application of Game Theory in Middle-Management: A Military Historical Study of the Role of the Middle-Manager as a Correlated Equilibrium Factor in the Decision-Making Process

2021 ◽  
Vol 2 (1) ◽  
pp. 51-64
Author(s):  
Quentin Commine ◽  
Jérémie Aboiron
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Middle Management ◽  
Decision Makers ◽  
Middle Manager ◽  
Historical Study ◽  
Correlated Equilibrium ◽  
Decision Making Process ◽  
Scientific Publications

The role of the manager, defined by innumerable scientific publications, is only rarely seen through the prism of game theory and its notions of equilibrium allowing decision-makers to optimize situations. The role of the middle-manager, mindful of the human factor and respectful toward his mission shall lead to a virtuous balance, can be defined in game theory as a correlated equilibrium in the sense of the game theorist Robert Aumann. Indeed, this kind of equilibrium goes further than the Nash equilibrium by introducing the notion of a common game and an intermediary embedded in the decision-making process and getting the strategy from his superiors to translate it to his subordinated staff. We use two military historical illustrations to illustrate this concept: the case of the Auftragstaktik refers to Sherman's "march to the sea" while the study of Lee's defeat at Gettysburg refers to the necessity of having capable subordinated staff to maximize an outcome. Throughout this study, we show and formalize the essential role of the middle-manager in the elaboration of effective decisions and processes.

The new futility? The rhetoric and role of “suffering” in pediatric decision-making

2019 ◽  
Vol 27 (1) ◽  
pp. 16-27
Author(s):  
Erica K Salter
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Medical Decision ◽  
Value Judgments ◽  
Medical Decisions ◽  
Surrogate Decision Makers ◽  
Surrogate Decision ◽  
Pediatric Decision Making

This article argues that while the presence and influence of “futility” as a concept in medical decision-making has declined over the past decade, medicine is seeing the rise of a new concept with similar features: suffering. Like futility, suffering may appear to have a consistent meaning, but in actuality, the concept is colloquially invoked to refer to very different experiences. Like “futility,” claims of patient “suffering” have been used (perhaps sometimes consciously, but most often unconsciously) to smuggle value judgments about quality of life into decision-making. And like “futility,” it would behoove us to recognize the need for new, clearer terminology. This article will focus specifically on secondhand claims of patient suffering in pediatrics, but the conclusions could be similarly applied to medical decisions for adults being made by surrogate decision-makers. While I will argue that suffering, like futility, is not sufficient wholesale justification for making unilateral treatment decisions, I will also argue that claims of patient suffering cannot be ignored, and that they almost always deserve some kind of response. In the final section, I offer practical suggestions for how to respond to claims of patient suffering.

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