Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists

Interobserver variation affects accuracy of inference in life history studies using cementochronology

Objective

Cementochronology is a method for assessing chronological age and identifying other life-history parameters (LHPs) from incremental lines of acellular extrinsic fibre cementum (AEFC) in most mammalian teeth. The aim of this study is to question the accuracy of this technique when used as a stand-alone age estimation method, and to examine how the number of observers may alter accuracy.

Design

This research is based on an extant clinical study conducted on 10 human teeth with the patients' anamnestic data. Nine observers used cementochronology to count AEFC incremental lines from 82 digital images. The counting was performed at three non-standardised areas on each image, totalling 246 counts per observer. Resultant observer counts were compared using the coefficient of variation method.

Results

The mean deviation of cementum estimated age from known chronological age of the participants in the study is 5.2 years.

Conclusion

Our study shows that further critical examination of the current cementochronology technique is essential, due to the subjectivity of line counting. The number of skilled observers in the study may improve the overall accuracy of the technique. These issues have wider implications, as many researchers rely on accurate scientific inferences being made by cementum-based studies to support or refute overarching demographic models and grand evolutionary narratives grounded by life history theory. Until this issue is resolved cementochronology should only be used alongside other age estimation methods.

Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology

Background

The integration of Artificial Intelligence (AI)-based technologies in medicine is advancing rapidly especially in the field of radiology. This however, is at a slow pace in Africa, hence, this study to evaluate the perspectives of Ghanaian radiologists.

Methods

Data for this cross-sectional prospective study was collected between September and November 2021 through an online survey and entered into SPSS for analysis. A Mann-Whitney U test assisted in checking for possible gender differences in the mean Likert scale responses on the radiologists' perspectives about AI in radiology. Statistical significance was set at P ≤ 0.05.

Results

The study comprised 77 radiologists, with more males (71.4%). 97.4% were aware of the concept of AI, with their initial exposure via conferences (42.9%). The majority of the respondents had average awareness (36.4%) and below average expertise (44.2%) in radiological AI usage. Most of the participants (54.5%) stated, they do not use AI in their practices. The respondents disagreed that AI will ultimately replace radiologists in the near future (average Likert score = 3.49, SD = 1.096) and that AI should be an integral part of the training of radiologists (average Likert score = 1.91, SD = 0.830).

Conclusion

Although the radiologists had positive opinions about the capabilities of AI, they exhibited an average awareness of and below average expertise in the usage of AI applications in radiology. They agreed on the potential life changing impact of AI and were of the view that AI will not replace radiologists but serve as a complement. There was inadequate radiological AI infrastructure in Ghana.

Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia

Objectives

This study aimed to gain insight into radiographers’ views on the application of artificial intelligence (AI) in Saudi Arabia by conducting a qualitative investigation designed to provide recommendations to assist radiographic workforce improvement.

Materials and methods

We conducted an online cross-sectional online survey of Saudi radiographers regarding perspectives on AI implementation, job security, workforce development, and ethics.

Results

In total, 562 valid responses were received. Most respondents (90.6%) believed that AI was the direction of diagnostic imaging. Among the respondents, 88.5% stated that AI would improve the accuracy of diagnosis. Some challenges in implementing AI in Saudi Arabia include the high cost of equipment, inadequate knowledge, radiologists’ fear of losing employment, and concerns related to potential medical errors and cyber threats.

Conclusion

Radiographers were generally positive about introducing AI to radiology departments. To integrate AI successfully into radiology departments, radiographers need training programs, transparent policies, and motivation.

Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

INTRODUCTION

The integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiographers' perspectives on the integration of AI into medical imaging.

METHODS

A cross-sectional online survey of registered Ghanaian radiographers was conducted within a 3-month period (February-April, 2020). The survey sought information relating to demography, general perspectives on AI and implementation issues. Descriptive and inferential statistics were used for data analyses.

RESULTS

A response rate of 64.5% (151/234) was achieved. Majority of the respondents (n = 122, 80.8%) agreed that AI technology is the future of medical imaging. A good number of them (n = 131, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. However, some expressed fears about AI-related errors (n = 126, 83.4%), while others expressed concerns relating to job security (n = 35, 23.2%). High equipment cost, lack of knowledge and fear of cyber threats were identified as some factors hindering AI implementation in Ghana.

CONCLUSIONS

The radiographers who responded to this survey demonstrated a positive attitude towards the integration of AI into medical imaging. However, there were concerns about AI-related errors, job displacement and salary reduction which need to be addressed. Lack of knowledge, high equipment cost and cyber threats could impede the implementation of AI in medical imaging in Ghana. These findings are likely comparable to most low resource countries and we suggest more education to promote credibility of AI in practice.

Artificial intelligence in diagnostic imaging: impact on the radiography profession

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.