Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers

AI for fracture diagnosis in clinical practice: Four approaches to systematic AI-implementation and their impact on AI-effectiveness

PURPOSE

Artificial Intelligence (AI) has been shown to enhance fracture-detection-accuracy, but the most effective AI-implementation in clinical practice is less well understood. In the current study, four approaches to AI-implementation are evaluated for their impact on AI-effectiveness.

MATERIALS AND METHODS

Retrospective single-center study based on all consecutive, around-the-clock radiographic examinations for suspected fractures, and accompanying clinical-practice radiologist-diagnoses, between January and March 2023. These image-sets were independently analysed by a dedicated bone-fracture-detection-AI. Findings were combined with radiologist clinical-practice diagnoses to simulate the four AI-implementation methods deemed most relevant to clinical workflows: AI-standalone (radiologist-findings not consulted); AI-problem-solving (AI-findings consulted when radiologist in doubt); AI-triage (radiologist-findings consulted when AI in doubt); and AI-safety net (AI-findings consulted when radiologist diagnosis negative). Reference-standard diagnoses were established by two senior musculoskeletal-radiologists (by consensus in cases of disagreement). Radiologist- and radiologist + AI diagnoses were compared for false negatives (FN), false positives (FP) and their clinical consequences. Experience-level-subgroups radiologists-in-training-, non-musculoskeletal-radiologists, and dedicated musculoskeletal-radiologists were analysed separately.

RESULTS

1508 image-sets were included (1227 unique patients; 40 radiologist-readers). Radiologist results were: 2.7 % FN (40/1508), 28 with clinical consequences; 1.2 % FP (18/1508), 2 received full-fracture treatments (11.1 %). All AI-implementation methods changed overall FN and FP with statistical significance (p < 0.001): AI-standalone 1.5 % FN (23/1508; 11 consequences), 6.8 % FP (103/1508); AI-problem-solving 3.2 % FN (48/1508; 31 consequences), 0.6 % FP (9/1508); AI-triage 2.1 % FN (32/1508; 18 consequences), 1.7 % FP (26/1508); AI-safety net 0.07 % FN (1/1508; 1 consequence), 7.6 % FP (115/1508). Subgroups show similar trends, except AI-triage increased FN for all subgroups except radiologists-in-training.

CONCLUSION

Implementation methods have a large impact on AI-effectiveness. These results suggest AI should not be considered for problem-solving or triage at this time; AI standalone performs better than either and may be a source of assistance where radiologists are unavailable. Best results were obtained implementing AI as safety net, which eliminates missed fractures with serious clinical consequences; even though false positives are increased, unnecessary treatments are limited.

What Are Patients' Perceptions and Attitudes Regarding the Use of Artificial Intelligence in Skin Cancer Screening and Diagnosis? Narrative Review

Artificial intelligence (AI) could enable early diagnosis of skin cancer; however, how AI should be implemented in clinical practice is debated. This narrative literature review (16 studies; 2012-2024) explored patient perceptions of AI in skin cancer screening and diagnosis. Patients were generally positive and perceived AI to increase diagnostic speed and accuracy. Patients preferred AI to augment a dermatologist's diagnosis rather than replace it. Patients were concerned that AI could lead to privacy breaches and clinicians deskilling and threaten doctor-patient relationships. Findings also highlight the complex nature of the impact of demographic, quality, and functional attributes on patients' attitudes toward AI.

For the busy clinical-imaging professional in an AI world: Gaining intuition about deep learning without math

Medical diagnostics comprise recognizing patterns in images, tissue slides, and symptoms. Deep learning algorithms (DLs) are well suited to such tasks, but they are black boxes in various ways. To explain DL Computer-Aided Diagnostic (CAD) results and their accuracy to patients, to manage or drive the direction of future medical DLs, to make better decisions with CAD, etc., clinical professionals may benefit from hands-on, under-the-hood lessons about medical DL. For those who already have some high-level knowledge about DL, the next step is to gain a more-fundamental understanding of DLs, which may help illuminate inside the boxes. The objectives of this Continuing Medical Education (CME) article include:Better understanding can come from relatable medical analogies and personally experiencing quick simulations to observe deep learning in action, akin to the way clinicians are trained to perform other tasks. We developed readily-implementable demonstrations and simulation exercises. We framed the exercises using analogies to breast cancer, malignancy and cancer stage as example diagnostic applications. The simulations revealed a nuanced relationship between DL output accuracy and the quantity and nature of the data. The simulation results provided lessons-learned and implications for the clinical world. Although we focused on DLs for diagnosis, they are similar to DLs for treatment (e.g. radiotherapy) so that treatment providers may also benefit from this tutorial.

The diagnosis of ASD with MRI: a systematic review and meta-analysis

While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the current diagnostic practice involves observation-based criteria. This study is a systematic review and meta-analysis of studies that aim to diagnose ASD using magnetic resonance imaging (MRI). The main objective is to describe the state of the art of diagnosing ASD using MRI in terms of performance metrics and interpretation. Furthermore, subgroups, including different MRI modalities and statistical heterogeneity, are analyzed. Studies that dichotomously diagnose individuals with ASD and healthy controls by analyses progressing from magnetic resonance imaging obtained in a resting state were systematically selected by two independent reviewers. Studies were sought on Web of Science and PubMed, which were last accessed on February 24, 2023. The included studies were assessed on quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate random-effects model was used for syntheses. One hundred and thirty-four studies were included comprising 159 eligible experiments. Despite the overlap in the studied samples, an estimated 4982 unique participants consisting of 2439 individuals with ASD and 2543 healthy controls were included. The pooled summary estimates of diagnostic performance are 76.0% sensitivity (95% CI 74.1-77.8), 75.7% specificity (95% CI 74.0-77.4), and an area under curve of 0.823, but uncertainty in the study assessments limits confidence. The main limitations are heterogeneity and uncertainty about the generalization of diagnostic performance. Therefore, comparisons between subgroups were considered inappropriate. Despite the current limitations, methods progressing from MRI approach the diagnostic performance needed for clinical practice. The state of the art has obstacles but shows potential for future clinical application.

Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT)

In recent years, the emergence of new-generation deep learning-based artificial intelligence (AI) tools has reignited enthusiasm about the potential of computer-assisted detection (CADe) and diagnosis (CADx) for screening mammography. For screening mammography, digital breast tomosynthesis (DBT) combined with acquired digital 2D mammography or synthetic 2D mammography is widely used throughout the United States. As of this writing in July 2024, there are six Food and Drug Administration (FDA)-cleared AI-based CADe/x tools for DBT. These tools detect suspicious lesions on DBT and provide corresponding scores at the lesion and examination levels that reflect likelihood of malignancy. In this article, we review the evidence supporting the use of AI-based CADe/x for DBT. The published literature on this topic consists of multireader, multicase studies, retrospective analyses, and two "real-world" evaluations. These studies suggest that AI-based CADe/x could lead to improvements in sensitivity without compromising specificity and to improvements in efficiency. However, the overall published evidence is limited and includes only two small postimplementation clinical studies. Prospective studies and careful postimplementation clinical evaluation will be necessary to fully understand the impact of AI-based CADe/x on screening DBT outcomes.

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Clinical applications of artificial intelligence in radiology

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience

OBJECTIVE

The purpose of this study is to assess the "real-world" impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center.

METHODS

Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations.

RESULTS

Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9-1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8-1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97-1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8-1.5).

CONCLUSION

Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.