An evidence-based approach to artificial intelligence education for medical students: A systematic review.

The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.

Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers.

Purpose: Length and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys. Approach: An nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements. Results: The experts resulted in length of 84.8±26.4 mm [95% CI: 80.0, 89.6] and a width of 51.8±10.5 mm [49.9, 53.7]. The algorithm resulted a length of 86.3±24.4 [81.5, 91.1] and a width of 47.1±12.8 [43.6, 50.6]. Experts, novices, and the algorithm did not statistically significant differ from one another (p>0.05). Bland-Altman analysis showed the algorithm produced a mean difference of 2.6 mm (SD = 1.2) from experts, compared to novices who had a mean difference of 3.7 mm (SD = 2.9 mm). For volumes, mean absolute difference was 47 mL (31%) consistent with ∼1 mm error in all three dimensions. Conclusions: This pilot study demonstrates the feasibility of an automatic tool to measure in vivo kidney biometrics of length, width, and volume from standard 2D ultrasound views with comparable accuracy and reproducibility to expert sonographers. Such a tool may enhance workplace efficiency, assist novices, and aid in tracking disease progression.

The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions.

OBJECTIVE: Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims. METHODS: B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed. DISCUSSION: The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation. CONCLUSION: We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Insights from teaching artificial intelligence to medical students in Canada.

Clinical artificial intelligence (AI) applications are rapidly developing but existing medical school curricula provide limited teaching covering this area. Here we describe an AI training curriculum we developed and delivered to Canadian medical undergraduates and provide recommendations for future training.

Alcohol Use Disorder: Neurobiology and Therapeutics.

Alcohol use disorder (AUD) encompasses the dysregulation of multiple brain circuits involved in executive function leading to excessive consumption of alcohol, despite negative health and social consequences and feelings of withdrawal when access to alcohol is prevented. Ethanol exerts its toxicity through changes to multiple neurotransmitter systems, including serotonin, dopamine, gamma-aminobutyric acid, glutamate, acetylcholine, and opioid systems. These neurotransmitter imbalances result in dysregulation of brain circuits responsible for reward, motivation, decision making, affect, and the stress response. Despite serious health and psychosocial consequences, this disorder still remains one of the leading causes of death globally. Treatment options include both psychological and pharmacological interventions, which are aimed at reducing alcohol consumption and/or promoting abstinence while also addressing dysfunctional behaviours and impaired functioning. However, stigma and social barriers to accessing care continue to impact many individuals. AUD treatment should focus not only on restoring the physiological and neurological impairment directly caused by alcohol toxicity but also on addressing psychosocial factors associated with AUD that often prevent access to treatment. This review summarizes the impact of alcohol toxicity on brain neurocircuitry in the context of AUD and discusses pharmacological and non-pharmacological therapies currently available to treat this addiction disorder.

Kidney Ultrasound for Nephrologists: A Review.

Ultrasound imaging is a key investigatory step in the evaluation of chronic kidney disease and kidney transplantation. It uses nonionizing radiation, is noninvasive, and generates real-time images, making it the ideal initial radiographic test for patients with abnormal kidney function. Ultrasound enables the assessment of both structural (form and size) and functional (perfusion and patency) aspects of kidneys, both of which are especially important as the disease progresses. Ultrasound and its derivatives have been studied for their diagnostic and prognostic significance in chronic kidney disease and kidney transplantation. Ultrasound is rapidly growing more widely accessible and is now available even in handheld formats that allow for bedside ultrasound examinations. Given the trend toward ubiquity, the current use of kidney ultrasound demands a full understanding of its breadth as it and its variants become available. We described the current applications and future directions of ultrasound imaging and its variants in the context of chronic kidney disease and transplantation in this review.

Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada.

BACKGROUND: Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. OBJECTIVE: This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. METHODS: The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. RESULTS: A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. CONCLUSIONS: We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.

Clinical validation of an audio-based uroflowmetry application in adult males.

INTRODUCTION: Uroflowmetry is a common test to evaluate lower urinary tract symptoms. Audio-based uroflowmetry is a novel, alternative approach that determines urine flow by measuring sound. Available as a smartphone application, it has potential for screening and monitoring common urological pathologies, particularly in out-of-office environments. This study is the first to evaluate audio-based uroflowmetry in a clinical setting against the gold standard. METHODS: Adult male patients (n=44) attending a general urology clinic were recruited. Audio-based uroflowmetry and conventional uroflowmetry were performed concurrently. Pearson correlation and Bland-Altman analysis were used to compare performance with respect to max flow, time to max flow, and total voiding time. Symmetric mean absolute percentage error (SMAPE) was used to compare curve shapes. Repeatability was evaluated separately in three healthy volunteers using repeat measures correlation. RESULTS: Among urology clinic patients, the correlation for max flow was 0.12. Correlation for time to max flow was 0.46, with limits of agreement of -120-165%. Correlation for total voiding time was 0.91, with limits of agreement of -41-38%. The SMAPE for curve shape was 32.6%, with corresponding accuracy of 67.4%. Among healthy volunteers, the repeat measures correlation for max flow was 0.72. CONCLUSIONS: Audio-based uroflowmetry was inconsistent in evaluating flow rate, attributable to high variability and difficult standardization for acoustic signals. Performance improved with respect to temporal variables, as well as flow curve shape. Further work evaluating intra-patient reliability and pathology-specific performance is required to fully evaluate audio-based uroflowmetry as a screening or monitoring tool.

Lichen Planus and Metabolic Syndrome: Is There a Relation?

BACKGROUND: Recent data suggests association of lichen planus (LP) with various systemic disorders. Relationship between LP and metabolic syndrome (MS) is not yet taken into account. MS has been associated with increased risk of cardiovascular diseases. Hence, earlier detection and treatment could potentially decrease mortality and improve the quality of life in these patients. OBJECTIVES: To find out the association of LP with MS. MATERIALS AND METHODS: About 100 LP patients and 50 healthy adults were investigated for fasting blood glucose (FBS) and lipid profile. MS was diagnosed as per National Cholesterol Education Program's Adult Treatment Panel III guidelines. RESULTS: Serum cholesterol, triglycerides, low density lipoprotein (LDL-C), and very low density lipoprotein (VLDL-C) values were significantly increased in cases as compared to controls (P < 0.05 in all). About 42% of patients showed raised FBS level as compared to 10% controls (P = 0.0003). MS was more prevalent in cases than in controls (43% versus 26% respectively, P = 0.045). Odds ratio was highest in FBS and waist circumference. LIMITATIONS: As the cases and controls are included from a local area, the result may differ from other parts of the world. CONCLUSION: Diabetes mellitus, dyslipidemia, and MS are seen more commonly in LP patients.

Acoustic Shadow Detection: Study and Statistics of B-Mode and Radiofrequency Data.

An acoustic shadow is an ultrasound artifact occurring at boundaries between significantly different tissue impedances, resulting in signal loss and a dark appearance. Shadow detection is important as shadows can identify anatomical features or obscure regions of interest. A study was performed to scan human participants (N = 37) specifically to explore the statistical characteristics of various shadows from different anatomy and with different transducers. Differences in shadow statistics were observed and used for shadow detection algorithms with a fitted Nakagami distribution on radiofrequency (RF) speckle or cumulative entropy on brightness-mode (B-mode) data. The fitted Nakagami parameter and entropy values in shadows were consistent across different transducers and anatomy. Both algorithms utilized adaptive thresholding, needing only the transducer pulse length as an input parameter for easy utilization by different operators or equipment. Mean Dice coefficients (± standard deviation) of 0.90 ± 0.07 and 0.87 ± 0.08 were obtained for the RF and B-mode algorithms, which is within the range of manual annotators. The high accuracy in different imaging scenarios indicates that the shadows can be detected with high versatility and without expert configuration. The understanding of shadow statistics can be used for more specialized techniques to be developed for specific applications in the future, including pre-processing for machine learning and automatic interpretation.

The Resident Report Card: A Tool for Operative Feedback and Evaluation of Technical Skills.

BACKGROUND: Competency-based medical education surgical curriculums will require frequent, recorded trainee performance evaluations. It is our hypothesis that written feedback after each operation can be used to chart surgical progress, can identify underperforming trainees, and will prove beneficial for resident learning. METHODS: The resident report card (RRC) is an online, easy-to-use evaluation tool designed to facilitate the creation and distribution of resident technical assessments. RRC data were collected from urologic trainees and analyzed using ANOVA and post hoc testing to confirm our hypothesis. A standardized survey was sent to residents, gauging their views on the RRC. RESULTS: Over a 5-y period, 958 RRCs with the resident listed as the primary operator were collected across 29 different procedures. Resident cohort and individual performance scores stratified by postgraduate year (PGY) were shown to significantly improve when all procedures (cohort, 6.5 ± 1.9 [PGY-1] to 9.1 ± 1.0 [PGY-5]; individual [resident M], 8.8 ± 1.8 [PGY-3] to 9.4 ± 0.7 [PGY-5], P < 0.01) and specific procedures (laparoscopic donor nephrectomy: cohort, 7.3 ± 1.3 [PGY-3] to 8.9 ± 1.0 [PGY-5]; individual [resident I], 7.2 ± 1.3 [PGY-3] to 9.5 ± 0.6 [PGY-5], P < 0.01) were analyzed. Individual residents were able to be compared to their own peer group and to the average scores across all evaluated residents. Surveyed residents were overwhelmingly positive about the RRC. CONCLUSIONS: The RRC adds further evidence to the fact that standardized, formative, and timely assessment can capture trainee performance over time and against comparator cohorts in an acceptable format to residents and academic training programs.

Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection.

Placental assessment through routine obstetrical ultrasound is often limited to documenting its location and ruling out placenta previa. However, many obstetrical complications originate from abnormal focal or global placental development. Technical difficulties in assessing the placenta as well as a lack of established objective criteria to classify echotexture are barriers to diagnosis of pathology by ultrasound imaging. As a first step towards the development of a computer aided placental assessment tool, we developed a fully automated method for placental segmentation using a convolutional neural network. The network contains a novel layer weighted by automated acoustic shadow detection to recognize artifacts specific to ultrasound. In order to develop a detection algorithm usable in different imaging scenarios, we acquired a dataset containing 1364 fetal ultrasound images from 247 patients acquired over 47 months was taken with different machines, operators, and at a range of gestational ages. Mean Dice coefficients for automated segmentation on the full dataset with and without the acoustic shadow detection layer were 0.92±0.04 and 0.91±0.03 when comparing to manual segmentation. Mean Dice coefficients on the subset of images containing acoustic shadows with and without acoustic shadow detection were 0.87±0.04 and 0.75±0.05. The method requires no user input to tune the detection. The automated placenta segmentation method can serve as a preprocessing step for further image analysis in artificial intelligence methods requiring large scale data processing of placental images.

Follow the light: projector-based augmented reality intracorporeal system for laparoscopic surgery.

A projector-based augmented reality intracorporeal system (PARIS) is presented that includes a miniature tracked projector, tracked marker, and laparoscopic ultrasound (LUS) transducer. PARIS was developed to improve the efficacy and safety of laparoscopic partial nephrectomy (LPN). In particular, it has been demonstrated to effectively assist in the identification of tumor boundaries during surgery and to improve the surgeon's understanding of the underlying anatomy. PARIS achieves this by displaying the orthographic projection of the cancerous tumor on the kidney's surface. The performance of PARIS was evaluated in a user study with two surgeons who performed 32 simulated robot-assisted partial nephrectomies. They performed 16 simulated partial nephrectomies with PARIS for guidance and 16 simulated partial nephrectomies with only an LUS transducer for guidance. With PARIS, there was a significant reduction [30% ([Formula: see text])] in the amount of healthy tissue excised and a trend toward a more accurate dissection around the tumor and more negative margins. The combined point tracking and reprojection root-mean-square error of PARIS was 0.8 mm. PARIS' proven ability to improve key metrics of LPN surgery and qualitative feedback from surgeons about PARIS supports the hypothesis that it is an effective surgical navigation tool.

Intra-operative ultrasound-based augmented reality guidance for laparoscopic surgery.

In laparoscopic surgery, the surgeon must operate with a limited field of view and reduced depth perception. This makes spatial understanding of critical structures difficult, such as an endophytic tumour in a partial nephrectomy. Such tumours yield a high complication rate of 47%, and excising them increases the risk of cutting into the kidney's collecting system. To overcome these challenges, an augmented reality guidance system is proposed. Using intra-operative ultrasound, a single navigation aid, and surgical instrument tracking, four augmentations of guidance information are provided during tumour excision. Qualitative and quantitative system benefits are measured in simulated robot-assisted partial nephrectomies. Robot-to-camera calibration achieved a total registration error of 1.0 ± 0.4 mm while the total system error is 2.5 ± 0.5 mm. The system significantly reduced healthy tissue excised from an average (±standard deviation) of 30.6 ± 5.5 to 17.5 ± 2.4 cm3 (p < 0.05) and reduced the depth from the tumor underside to cut from an average (±standard deviation) of 10.2 ± 4.1 to 3.3 ± 2.3 mm (p < 0.05). Further evaluation is required in vivo, but the system has promising potential to reduce the amount of healthy parenchymal tissue excised.