How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis

The unintended consequences of artificial intelligence in paediatric radiology

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.

A holistic approach to implementing artificial intelligence in radiology

OBJECTIVE

Despite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.

MATERIALS AND METHODS

We conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.

RESULTS

This study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.

CONCLUSION

This study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.

CRITICAL RELEVANCE STATEMENT

Adoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.

KEY POINTS

1. Practical and actionable insights into successful AI implementation in radiology are lacking. 2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation 3. Holistic approach aids organisations to create sustainable value through AI implementation.

An artificial intelligence algorithm for the classification of sphenoid sinus pneumatisation on sinus computed tomography scans

BACKGROUND

Classifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can create algorithms that can learn from data without being programmed with explicit rules and have shown utility in radiological image classification.

OBJECTIVE

To determine if a trained CNN can accurately classify sphenoid sinus pneumatisation on CT sinus imaging.

METHODS

Sagittal slices through the natural ostium of the sphenoid sinus were extracted from retrospectively collected bone-window CT scans of the paranasal sinuses for consecutive patients over 6 years. Two blinded Otolaryngology residents reviewed each image and classified the sphenoid sinus pneumatisation as either conchal, presellar or sellar. An AI algorithm was developed using the Microsoft Azure Custom Vision deep learning platform to classify the pattern of pneumatisation.

RESULTS

Seven hundred eighty images from 400 patients were used to train the algorithm, which was then tested on a further 118 images from 62 patients. The algorithm achieved an accuracy of 93.2% (95% confidence interval [CI] 87.1-97.0), 87.3% (95% CI 79.9-92.7) and 85.6% (95% CI 78.0-91.4) in correctly identifying conchal, presellar and sellar sphenoid pneumatisation, respectively. The overall weighted accuracy of the CNN was 85.9%.

CONCLUSION

The CNN described demonstrated a moderately accurate classification of sphenoid pneumatisation subtypes on CT scans. The use of CNN-based assistive tools may enable surgeons to achieve safer operative planning through routine automated reporting allowing greater resources to be directed towards the identification of pathology.

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021

OBJECTIVES

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?

METHODS

We systematically analyze 393 AI applications developed for supporting diagnostic radiology workflow. We collected qualitative and quantitative data by analyzing around 1250 pages of documents retrieved from companies' websites and legal documents. Five investigators read and interpreted collected data, extracted the features and functionalities of the AI applications, and finally entered them into an excel file for identifying the patterns.

RESULTS

Over the last 2 years, we see an increase in the number of AI applications (43%) and number of companies offering them (34%), as well as their average age (45%). Companies claim various value propositions related to increasing the "efficiency" of radiology work (18%)-e.g., via reducing the time and cost of performing tasks and reducing the work pressure-and "quality" of offering medical services (31%)-e.g., via enhancing the quality of clinical decisions and enhancing the quality of patient care, or both of them (28%). To legitimize and support their value propositions, the companies use multiple strategies simultaneously, particularly by seeking legal approvals (72%), promoting their partnership with medical and academic institutions (75%), highlighting the expertise of their teams (56%), and showcasing examples of implementing their solutions in practice (53%).

CONCLUSIONS

Although providers of AI applications claim a wide range of value propositions, they often provide limited evidence to show how their solutions deliver such systematic values in clinical practice.

KEY POINTS

• AI applications in radiology continue to grow in number and diversity. • Companies offering AI applications claim various value propositions and use multiple ways to legitimize these propositions. • Systematic scientific evidence showing the actual effectiveness of AI applications in clinical context is limited.

UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments

INTRODUCTION

Radiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems.

METHODS

A Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only.

RESULTS

86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust.

CONCLUSIONS

Responses indicate that AI will have a strong impact on reporting radiographers' decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions.

IMPLICATIONS FOR PRACTICE

This survey clarifies UK reporting radiographers' perceptions of AI, used for image interpretation, highlighting key issues with AI integration.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

In the Land of the Blind, the One-Eyed Man Is King: Knowledge Brokerage in the Age of Learning Algorithms

This paper presents research on how knowledge brokers attempt to translate opaque algorithmic predictions. The research is based on a 31-month ethnographic study of the implementation of a learning algorithm by the Dutch police to predict the occurrence of crime incidents and offers one of the first empirical accounts of algorithmic brokers. We studied a group of intelligence officers, who were tasked with brokering between a machine learning community and a user community by translating the outcomes of the learning algorithm to police management. We found that, as knowledge brokers, they performed different translation practices over time and enacted increasingly influential brokerage roles, namely, those of messenger, interpreter, and curator. Triggered by an impassable knowledge boundary yielded by the black-boxed machine learning, the brokers eventually acted like “kings in the land of the blind” and substituted the algorithmic predictions with their own judgments. By emphasizing the dynamic and influential nature of algorithmic brokerage work, we contribute to the literature on knowledge brokerage and translation in the age of learning algorithms.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.