Ethics, Artificial Intelligence, and Radiology

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with 177Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with 177Lu-prostate-specific membrane antigen and 177Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this "one-size-fits-all" approach to theranostics, through development of a symbiotic relationship with physicians. Combining quantitative data collection, collation, and analytic computing power of AI algorithms with the clinical expertise, empathy, and personal care of patients by their physician envisions a new paradigm in theranostic reporting for molecular imaging and radioligand treatment of cancer. Human-AI interaction will facilitate the compilation of a comprehensive, integrated nuclear medicine report. This holistic report would incorporate radiomics to quantitatively analyze diagnostic digital imaging and prospectively calculate the radiation absorbed dose to tumor and critical normal organs. The therapy activity could then be accurately prescribed to deliver a preordained, effective, tumoricidal radiation absorbed dose to tumor, while minimizing toxicity in the particular patient. Post-therapy quantitative imaging would then validate the actual dose delivered and sequential pre- and post-treatment dosimetry each cycle would allow individual dose prescription and monitoring over the entire course of theranostic treatment. Furthermore, the nuclear medicine report would use AI analysis to predict likely clinical outcome, predicated upon AI definition of tumor molecular biology, pathology, and genomics, correlated with clinical history and laboratory data. Such synergistic comprehensive reporting will enable self-assurance of the nuclear physician who will necessarily be deemed personally responsible and accountable for the theranostic clinical outcome. Paradoxically, AI may thus be expected to enhance the practice of phronesis by the nuclear physician and foster a truly empathic trusting relationship with the cancer patient.

Integrating Ethical Principles Into the Regulation of AI-Driven Medical Software

In recent years, a sharp increase in artificial intelligence (AI)-based software as medical devices has been seen in the United States and the European Union. Despite the huge potential of these devices in alleviating suffering through rapid identification and early intervention, their adoption in clinical practice has remained relatively slow due to ethical questions surrounding their usage. Even though there is no universal framework for the approval of these devices, the guiding principles behind individual regulatory bodies almost stay the same, with some more focused on the technical aspect while others involving the ethical aspects as well. The International Medical Device Regulators Forum devised a SaMD Working Group to outline the essential controls guiding the approval of these devices, but there is a lack of a structured approach for the regulatory approval process. This article outlines the principles of medical ethics, such as autonomy, beneficence, and fair distribution of healthcare sources, and how they relate to the use of AI-based devices. The core regulatory guidelines are then viewed in light of these ethical principles. We recommend that a comprehensive regulatory framework with integration of principles of medical ethics be made public. Though no universally accepted framework is available, regulating quality management, risk assessment, and data privacy would help build trust to promote the adoption of AI in healthcare.

Update on ethical aspects in clinical research: Addressing concerns in the development of new AI tools in radiology

The analysis of ethical aspects in clinical research has always been a challenge and has required constant updates. In short, research ethics is the set of specific principles, rules, and norms of behavior that a research community has decided are appropriate and fair under the premise that research must be valid, reliable, legitimate, and representative. This non-systematic review brings some ethical concerns that should be considered within the scientific community. Many studies and the development of new artificial intelligence (AI) tools, especially in radiology, make it necessary for the radiology research community to promote debates and establish ethical standards for the practice and development of new AI tools.

Ethical, legal, and regulatory landscape of artificial intelligence in Australian healthcare and ethical integration in radiography: A narrative review

This narrative review explores the ethical, legal, and regulatory landscape of AI integration in Australian healthcare, focusing on radiography. It examines the current legislative framework, assesses the trust and reliability of AI tools, and proposes future directions for ethical AI integration in radiography. AI systems significantly enhance diagnostic radiography by improving diagnostic accuracy and efficiency in stroke detection, brain imaging, and chest reporting. However, AI raises substantial ethical concerns due to its 'black-box' nature and potential biases in training data. The Therapeutic Goods Administration's reforms in Australia, though comprehensive, fall short of fully addressing issues related to the trustworthiness and legal liabilities of AI tools. Adopting a comprehensive research strategy that includes doctrinal, comparative, and public policy analyses will facilitate an understanding of international practices, particularly from countries with similar legal systems, and help guide Australia in refining its regulatory framework. For an ethical future in radiography, a robust, multi-disciplinary approach is required to prioritize patient safety, data privacy, and equitable AI use. A framework that balances technological innovation with ethical and legal integrity is essential for advancing healthcare while preserving trust and transparency. Healthcare professionals, policymakers, and AI developers must collaborate to establish a resilient, equitable, and transparent healthcare system. Future research should focus on multi-disciplinary methodologies, combining doctrinal, comparative, and public policy research to provide comprehensive insights. This approach will guide Australia in creating a more inclusive and ethically sound legal framework for AI in healthcare, ensuring its ethical and beneficial integration into radiography.

Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning

BACKGROUND AND OBJECTIVE

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level.

METHODS

In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units.

RESULTS

Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures.

CONCLUSION

This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.

Future Perspective of Risk Prediction in Aesthetic Surgery: Is Artificial Intelligence Reliable?

BACKGROUND

Artificial intelligence (AI) techniques are showing significant potential in the medical field. The rapid advancement in artificial intelligence methods suggests their soon-to-be essential role in physicians' practices.

OBJECTIVES

In this study, we sought to assess and compare the readability, clarity, and precision of medical knowledge responses provided by 3 large language models (LLMs) and informed consent forms for 14 common aesthetic surgical procedures, as prepared by the American Society of Plastic Surgeons (ASPS).

METHODS

The efficacy, readability, and accuracy of 3 leading LLMs, ChatGPT-4 (OpenAI, San Francisco, CA), Gemini (Google, Mountain View, CA), and Copilot (Microsoft, Redmond, WA), was systematically evaluated with 14 different prompts related to the risks of 14 common aesthetic procedures. Alongside these LLM responses, risk sections from the informed consent forms for these procedures, provided by the ASPS, were also reviewed.

RESULTS

The risk factor segments of the combined general and specific operation consent forms were rated highest for medical knowledge accuracy (P < .05). Regarding readability and clarity, the procedure-specific informed consent forms, including LLMs, scored highest scores (P < .05). However, these same forms received the lowest score for medical knowledge accuracy (P < .05). Interestingly, surgeons preferred patient-facing materials created by ChatGPT-4, citing superior accuracy and medical information compared to other AI tools.

CONCLUSIONS

Physicians prefer patient-facing materials created by ChatGPT-4 over other AI tools due to their precise and comprehensive medical knowledge. Importantly, adherence to the strong recommendation of ASPS for signing both the procedure-specific and the general informed consent forms can avoid potential future complications and ethical concerns, thereby ensuring patients receive adequate information.

Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study

BACKGROUND

As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC.

OBJECTIVE

This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning.

METHODS

A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model).

RESULTS

The overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients.

CONCLUSIONS

The hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.

Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study

OBJECTIVES

We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.

METHODS

A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.

RESULTS

Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.

CONCLUSIONS

Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum.

CLINICAL RELEVANCE STATEMENT

This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula.

KEY POINTS

• Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

OBJECTIVE

To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology.

METHODS

This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value.

RESULTS

RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring.

CONCLUSION

The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology.

CRITICAL RELEVANCE STATEMENT

The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation.

KEYPOINTS

• Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.

Radiomics in Breast Imaging: Future Development

Breast cancer is the most common and most commonly diagnosed non-skin cancer in women. There are several risk factors related to habits and heredity, and screening is essential to reduce the incidence of mortality. Thanks to screening and increased awareness among women, most breast cancers are diagnosed at an early stage, increasing the chances of cure and survival. Regular screening is essential. Mammography is currently the gold standard for breast cancer diagnosis. In mammography, we can encounter problems with the sensitivity of the instrument; in fact, in the case of a high density of glands, the ability to detect small masses is reduced. In fact, in some cases, the lesion may not be particularly evident, it may be hidden, and it is possible to incur false negatives as partial details that may escape the radiologist's eye. The problem is, therefore, substantial, and it makes sense to look for techniques that can increase the quality of diagnosis. In recent years, innovative techniques based on artificial intelligence have been used in this regard, which are able to see where the human eye cannot reach. In this paper, we can see the application of radiomics in mammography.

A Scoping Review of Mobile Apps in Plastic Surgery: Patient Care, Trainee Education, and Professional Development

Over the past 10 years, smartphones have become ubiquitous, and mobile apps serve a seemingly endless number of functions in our everyday lives. These functions have entered the realm of plastic surgery, impacting patient care, education, and delivery of services. This article reviews the current uses of plastic surgery mobile apps, app awareness within the plastic surgery community, and the ethical issues surrounding their use in patient care.

Methods

A scoping review of electronically available literature within PubMed, Embase, and Scopus databases was conducted in two waves in November and May 2022. Publications discussing mobile application use in plastic surgery were screened for inclusion.

Results

Of the 80 nonduplicate publications retrieved, 20 satisfied the inclusion criteria. Articles acquired from the references of these publications were reviewed and summarized when relevant. The average American Society of Plastic Surgeons evidence rating of the publications was 4.2. Applications could be categorized broadly into three categories: patient care and surgical applications, professional development and education, and marketing and practice development.

Conclusions

Mobile apps related to plastic surgery have become an abundant resource for patients, attending surgeons, and trainees. Many help bridge gaps in patient care and surgeon-patient communication, and facilitate marketing and practice development. Others make educational content more accessible to trainees and performance assessment more efficient and equitable. The extent of their impact on patient decision-making and expectations has not been completely elucidated.

A qualitative study to explore opinions of Saudi Arabian radiologists concerning AI-based applications and their impact on the future of the radiology

Objective

The aim of this study was to explore opinions and views towards radiology AI among Saudi Arabian radiologists including both consultants and trainees.

Methods

A qualitative approach was adopted, with radiologists working in radiology departments in the Western region of Saudi Arabia invited to participate in this interview-based study. Semi-structured interviews (n = 30) were conducted with consultant radiologists and trainees. A qualitative data analysis framework was used based on Miles and Huberman's philosophical underpinnings.

Results

Several factors, such as lack of training and support, were attributed to the non-use of AI-based applications in clinical practice and the absence of radiologists' involvement in AI development. Despite the expected benefits and positive impacts of AI on radiology, a reluctance to use AI-based applications might exist due to a lack of knowledge, fear of error and concerns about losing jobs and/or power. Medical students' radiology education and training appeared to be influenced by the absence of a governing body and training programmes.

Conclusion

The results of this study support the establishment of a governing body or national association to work in parallel with universities in monitoring training and integrating AI into the medical education curriculum and residency programmes.

Advances in knowledge

An extensive debate about AI-based applications and their potential effects was noted, and considerable exceptions of transformative impact may occur when AI is fully integrated into clinical practice. Therefore, future education and training programmes on how to work with AI-based applications in clinical practice may be recommended.

Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms

BACKGROUND

Germinal matrix hemorrhage-intraventricular hemorrhage is among the most common intracranial complications in premature infants. Early detection is important to guide clinical management for improved patient prognosis.

OBJECTIVE

The purpose of this study was to assess whether a convolutional neural network (CNN) can be trained via transfer learning to accurately diagnose germinal matrix hemorrhage on head ultrasound.

MATERIALS AND METHODS

Over a 10-year period, 400 head ultrasounds performed in patients ages 6 months or younger were reviewed. Key sagittal images at the level of the caudothalamic groove were obtained from 200 patients with germinal matrix hemorrhage and 200 patients without hemorrhage; all images were reviewed by a board-certified pediatric radiologist. One hundred cases were randomly allocated from the total for validation and an additional 100 for testing of a CNN binary classifier. Transfer learning and data augmentation were used to train the model.

RESULTS

The median age of patients was 0 weeks old with a median gestational age of 30 weeks. The final trained CNN model had a receiver operating characteristic area under the curve of 0.92 on the validation set and accuracy of 0.875 on the test set, with 95% confidence intervals of [0.86, 0.98] and [0.81, 0.94], respectively.

CONCLUSION

A CNN trained on a small set of images with data augmentation can detect germinal matrix hemorrhage on head ultrasounds with strong accuracy.

Trustworthy Artificial Intelligence in Medical Imaging

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.

Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

PURPOSE

Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel's familiarity with AI applications and its usefulness in clinical practice were investigated.

METHODS

A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants' knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants' trust level was also measured; Kruskal-Wallis test was used to examine differences between groups.

RESULTS

A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).

CONCLUSION

Radiology personnel's knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.

Artificial Intelligence in Plastic Surgery: Applications and Challenges

New developments in artificial intelligence (AI) offer opportunities to enhance plastic surgery practice, research, and education. In this article, we review relevant AI tools and applications, including machine learning, reinforcement learning, and natural language processing. Our own Markov decision process for keloid treatment illustrates how these models are developed and can be used to enhance decision-making in clinical practice. Finally, we discuss challenges of implementing AI and knowledge gaps that must be addressed to successfully apply AI in plastic surgery. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?

Objective

To test medical students' perceptions of the impact of artificial intelligence (AI) on radiology and the influence of these perceptions on their choice of radiology as a lifetime career.

Methods

A cross-sectional multicenter survey of medical students in Saudi Arabia was conducted in April 2019.

Results

Of the 476 respondents, 34 considered radiology their first specialty choice, 26 considered it their second choice, and 65 considered it their third choice. Only 31% believed that AI would replace radiologists in their lifetime, while 44.8% believed that AI would minimize the number of radiologists needed in the future. Approximately 50% believed they had a good understanding of AI; however, when knowledge of AI was tested using five questions, on average, only 22% of the questions were answered correctly. Among the respondents who ranked radiology as their first choice, 58.8% were anxious about the uncertain impact of AI on radiology. The number of respondents who ranked radiology as one of their top three choices increased by 14 when AI was not a consideration. Radiology conferences and the opinions of radiologists had the most influence on the respondents' preferences for radiology.

Conclusion

The worry that AI might displace radiologists in the future had a negative influence on medical students' consideration of radiology as a career. Academic radiologists are encouraged to educate their students about AI and its potential impact when students are considering radiology as a lifetime career choice.

Advances in knowledge

Rapid advances of AI in radiology will certainly impact the specialty, the concern of AI impact on radiology had negative influence in our participants and investing in AI education and is highly recommended.

Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

Background:

Artificial intelligence (AI) in healthcare delivery has become an important area of research due to the rapid progression of technology, which has allowed the growth of many processes historically reliant upon human input. AI has become particularly important in plastic surgery in a variety of settings. This article highlights current applications of AI in plastic surgery and discusses future implications. We further detail ethical issues that may arise in the implementation of AI in plastic surgery.

Methods:

We conducted a systematic literature review of all electronically available publications in the PubMed, Scopus, and Web of Science databases as of February 5, 2020. All returned publications regarding the application of AI in plastic surgery were considered for inclusion.

Results:

Of the 89 novel articles returned, 14 satisfied inclusion and exclusion criteria. Articles procured from the references of those of the database search and those pertaining to historical and ethical implications were summarized when relevant.

Conclusions:

Numerous applications of AI exist in plastic surgery. Big data, machine learning, deep learning, natural language processing, and facial recognition are examples of AI-based technology that plastic surgeons may utilize to advance their surgical practice. Like any evolving technology, however, the use of AI in healthcare raises important ethical issues, including patient autonomy and informed consent, confidentiality, and appropriate data use. Such considerations are significant, as high ethical standards are key to appropriate and longstanding implementation of AI.

Sharing and Selling Images: Ethical and Regulatory Considerations for Radiologists

Opportunities to share or sell images are common in radiology. But because these images typically originate as protected health information, their use admits a host of ethical and regulatory considerations. This article discusses four scenarios that reflect data sharing or selling arrangements in radiology, especially as they might occur in "big data" systems or applications. The objective of this article is to acquaint radiologists with a variety of regulatory standards and ethical perspectives that pertain to certain data use agreements, such that the attitudes and practices of data holders and their sharers or purchasers can withstand ethical or regulatory scrutiny and not invite undesirable outcomes.

Ethical considerations for artificial intelligence: an overview of the current radiology landscape

Artificial intelligence (AI) has great potential to accelerate scientific discovery in medicine and to transform healthcare. In radiology, AI is about to enter into clinical practice and has a wide range of applications covering the whole diagnostic workflow. However, AI applications are not smooth sailing. It is crucial to understand the potential risks and hazards that come with this new technology. We have to implement AI in the best possible way to reflect the time-honored ethical and legal standards while ensuring the adequate protection of patient interests. These issues are discussed under the light of core biomedical ethics principles and principles for AI-specific ethical challenges while giving an overview of the statements that were proposed for the ethics of AI applications in radiology.

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

OBJECTIVE

To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.

METHOD

A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.

RESULTS

The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.

CONCLUSIONS

The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

The ethics of AI in health care: A mapping review

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be 'ethically mindful? A series of screening stages were carried out-for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)-yielding a total of 156 papers that were included in the review. We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new 'AI winter' could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

Artificial Intelligence in Radiology-Ethical Considerations

Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.

Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers

As artificial intelligence (AI) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and AI developers with the best interests of the patients. Radiologists know that supporting research into AI and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. This provides an incentive to oppose AI in various ways. AI developers have an incentive to hype their discoveries to gain attention. This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of AI in radiology will be partially determined by radiologist and AI researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the AI transformation to the patients.

The ethical adoption of artificial intelligence in radiology

Artificial intelligence (AI) is rapidly transforming healthcare—with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: training, integration and regulation.

For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics.

AI needs to be safely integrated with radiologists-in-the-loop: guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop—thus enabling a dynamic best prediction system.

In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a sustainable and transparent system.

An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions

Background

Deep learning-based computer-aided diagnosis (CAD) is an important method in aiding diagnosis for radiologists. We investigated the accuracy of a deep learning-based CAD in classifying breast lesions with different histological types.

Methods

A total of 448 breast lesions were detected on ultrasound (US) and classified by an experienced radiologist, a resident and deep learning-based CAD respectively. The pathological results of the lesions were chosen as the golden standard. The diagnostic performances of the three raters in different pathological types were analyzed.

Results

For the overall diagnostic performance, deep learning-based CAD presented a significantly higher specificity (76.96%) compared with the two radiologists. The area under ROC of CAD was almost equal with the experienced radiologist (0.81 vs. 0.81), while significantly higher than the resident (0.81 vs. 0.70, P<0.0001). In the benign lesions, deep learning-based CAD had a higher accuracy than both the two radiologists, which correctly classified as benign lesions in 119/135 of fibroadenomas (88.1%), 25/35 of adenosis (71.4%), 14/27 of intraductal papillary tumors (51.9%), 5/10 of inflammation (50%), and 4/8 of sclerosing adenosis (50%). But only the differences between CAD and the two radiologists in fibroadenomas had statistical significance (P=0.0011 and P=0.0313), and the differences between CAD and the resident in adenosis had statistical significance (P=0.012). In the malignant lesions, 151/168 of invasive ductal carcinomas (89.9%), 21/29 of ductal carcinoma in situ (DCIS) (72.4%) and 6/7 of invasive lobular carcinomas (85.7%) were diagnosed as malignancies by deep learning-based CAD, with no significant differences between CAD and the two radiologists.

Conclusions

In the diagnosis of these common types of breast lesions, deep learning-based CAD had a satisfying performance. Deep learning-based CAD had a better performance in the breast benign lesions, especially in fibroadenomas and adenosis. Therefore, deep learning-based CAD is a promising supplemental tool to US to increase the specificity and avoid unnecessary benign biopsies.

Data Engineering for Machine Learning in Women's Imaging and Beyond

OBJECTIVE. Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the quality of the data used for model development. This article aims to provide radiologists and radiology researchers with an understanding of the core elements of data preparation for machine learning research. We cover key concepts from an engineering perspective, including databases, data integrity, and characteristics of data suitable for machine learning projects, and from a clinical perspective, including the HIPAA, patient consent, avoidance of bias, and ethical concerns related to the potential to magnify health disparities. The focus of this article is women's imaging; nonetheless, the principles described apply to all domains of medical imaging. CONCLUSION. Machine learning research is inherently interdisciplinary: effective collaboration is critical for success. In medical imaging, radiologists possess knowledge essential for data engineers to develop useful datasets for machine learning model development.

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Patient Safety in Medical Imaging: a joint paper of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS)

The fundamental professional roles of radiographers and radiologists are focused on providing benefit to patients with our skills, while maintaining their safety at all times. There are numerous patient safety issues in radiology which must be considered. These encompass: protection from direct harm arising from the techniques and technologies we use; ensuring physical and psychological well-being of patients while under our care; maintaining the highest possible quality of service provision; and protecting the staff to ensure they can deliver safe services. This paper summarises the key categories of safety issues in the provision of radiology services, from the joint perspectives of radiographers and radiologists, and provides references for further reading in all major relevant areas.This is a joint statement of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS), published simultaneously in Insights into Imaging [DOI:10.1186/s13244-019-0721-y] and Radiography (DOI: 10.1016/j.radi.2019.01.009).

What the radiologist should know about artificial intelligence - an ESR white paper

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.

Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology

The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA's role in leading, organizing, and catalyzing change during this important time in radiology. © RSNA, 2019.

Can Contrast-Enhanced Ultrasound Increase or Predict the Success Rate of Testicular Sperm Aspiration in Patients With Azoospermia?

OBJECTIVE. The objective of our study was to determine whether contrast-enhanced ultrasound (CEUS) perfusion measurements obtained before testicular sperm aspiration (TESA) can improve or predict sperm retrieval (SR) outcomes of TESA in patients with azoospermia. SUBJECTS AND METHODS. Between May 2017 and January 2018, 70 patients with azoospermia (mean age, 29 years; age range, 22-41 years) underwent testes CEUS within 10 days before TESA. Major perfusion areas were visually chosen, and their ranges were recorded. The other areas were defined as minor perfusion. CEUS quantitative features were acquired for both the main perfusion area and whole testis. Testis tissue biopsies were taken for both major and minor perfusion areas by cognitive fusion, and SR outcomes were compared. Associations between testicular volume, quantitative CEUS features, and SR outcomes were analyzed. RESULTS. Twenty-four men were found to have obstructive azoospermia (OA), and the remaining 46 had nonobstructive azoospermia (NOA). All patients with OA had spermatozoa in biopsy. Only one patient with NOA had spermatozoa in the major perfusion area but not the minor perfusion area; the other patients with NOA had the same SR outcomes in both major and minor perfusion areas. In patients with NOA, both wash-in and washout CEUS features were correlated with the success of SR in TESA. CONCLUSION. CEUS-guided TESA with cognitive fusion cannot yield improved SR outcomes of TESA in patients with NOA, possibly because of imprecise correlation between biopsy sites and main perfusion area analyzed by CEUS; however, quantitative CEUS features can be useful predictors of the success of SR.