Evaluation of User-Prosthesis-Interfaces for sEMG-Based Multifunctional Prosthetic Hands

Fajardo, Julio; Maldonado, Guillermo; Cardona, Diego; Ferman, Victor; Rohmer, Eric

doi:10.3390/s21217088

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…Due to the growing amount of health data combined with the desire to gain ground in precision medicine, artificial intelligence (AI) is advancing at a rapid pace across the health care sector [ 1 ]. AI applications in medicine enable the analysis of a wide variety of health data types, ranging from web-based applications using machine learning (ML) to physical applications such as intelligent prostheses [ 2 ] and sophisticated robots [ 3 ]. ML is a subset of AI using large data inputs and outputs with the goal of recognizing patterns leading to autonomous recommendations or decisions.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Bias in Health Care: Web-Based Survey

Vorisek¹,

Stellmach²,

Mayer³

et al. 2023

J Med Internet Res

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Background Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. Objective This study’s objective was to survey AI specialists in health care to investigate developers’ perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. Methods A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. Results A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. Conclusions This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.

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Section: Introductionmentioning

confidence: 99%

“…This study set out to answer the following research questions from the AI developers' perspective: (1) How fair are current AI developments? (2) What is preventing fair AI algorithms? and (3) What data are used to train AI algorithms?…”

Section: Introductionmentioning

confidence: 99%