AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Artificial intelligence and explanation: How, why, and when to explain black boxes.

Artificial intelligence (AI) is infiltrating nearly all fields of science by storm. One notorious property that AI algorithms bring is their so-called black box character. In particular, they are said to be inherently unexplainable algorithms. Of course, such characteristics would pose a problem for the medical world, including radiology. The patient journey is filled with explanations along the way, from diagnoses to treatment, follow-up, and more. If we were to replace part of these steps with non-explanatory algorithms, we could lose grip on vital aspects such as finding mistakes, patient trust, and even the creation of new knowledge. In this article, we argue that, even for the darkest of black boxes, there is hope of understanding them. In particular, we compare the situation of understanding black box models to that of understanding the laws of nature in physics. In the case of physics, we are given a 'black box' law of nature, about which there is no upfront explanation. However, as current physical theories show, we can learn plenty about them. During this discussion, we present the process by which we make such explanations and the human role therein, keeping a solid focus on radiological AI situations. We will outline the AI developers' roles in this process, but also the critical role fulfilled by the practitioners, the radiologists, in providing a healthy system of continuous improvement of AI models. Furthermore, we explore the role of the explainable AI (XAI) research program in the broader context we describe.

Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction.

ABSTRACT: Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.

Psychoanalytic Meta-Theory: A Modern Ego Psychology View.

This article describes the meta-theory level of psychoanalytic theory and uses it to show the commonalities of the major metapsychologies.

Religious delusions in an evangelical Christian woman with anorexia nervosa.

This case report describes the history and hospital course of a 42-year-old devout evangelical Christian woman with a long standing history of anorexia nervosa, binge/purge type, who developed religious delusions, including the conviction that God was prohibiting her from eating. The discussion emphasizes the difficulties of diagnosing and treating psychosis in devout individuals, and the interplay between anorexia, psychosis, and religion.

Transference and countertransference to medication and its implications for ego function.

Transference to medication can provide important information about specific ego dysfunction in sicker patients who often need medication. Whether positive or negative or both in content, the organization of the experience provides one example of the illness' effect on the patients' ego and can therefore be a specific diagnostic assessment strategy. Early resistances to medication may reveal the nature of resistances to the therapeutic alliance and to higher-level ego function. Understanding this can guide verbal and pharmacological interventions to strengthen ego function. Countertransference can similarly be helpful because it, too, can be a highly specific diagnostic indicator.

Medical student dreams about medical school: the unconscious developmental process of becoming a physician.

This paper is a report on a collection of almost four hundred dreams of medical students and postgraduate trainees with the manifest content about medical training. It is a unique dream collection from a defined population that experiences a developmental sequence of observable, reality events. The reality events appear in the manifest content of the dreams along with their symbolic alterations. The dreams are used as a psychodynamic database. The data may illustrate which reality experiences seem psychologically formative, their emotional developmental sequences and their specific emotional content. This is a pilot project exploring whether dream material collected from a discrete task group might give information about a group's emotional adaptation. The dreams seem to show an unconscious developmental process in response to medical training and becoming a physician that unfolds in overlapping stages as trainees learn to master skills and tolerate care-giving responsibility for human life. A progressive, unconscious hero-healer fantasy seems to form. It becomes elaborated in masochistic and then sadistic fantasies. These fantasies are evoked by, and used as a defense against, inevitable but painful anxieties of emotional adaptation to medical education experiences.