Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being ...developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.
The rapid development of artificial intelligence (AI) has led to its widespread use in multiple industries, including healthcare. AI has the potential to be a transformative technology that will ...significantly impact patient care. Particularly, AI has a promising role in radiology, in which computers are indispensable and new technological advances are often sought out and adopted early in clinical practice. We present an overview of the basic definitions of common terms, the development of an AI ecosystem in imaging and its value in mitigating the challenges of implementation in clinical practice.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.
We ...audited publicly available details of regulated AI/ML algorithms in imaging from 2008 until April 2021. We reviewed 127 regulated software (118 AI/ML) to classify information related to their parent company, subspecialty, body area and specific anatomy type, imaging modality, date of FDA clearance, indications for use, target pathology (such as trauma) and findings (such as fracture), technique (CAD triage, CAD detection and/or characterization, CAD acquisition or improvement, and image processing/quantification), product performance, presence, type, strength and availability of clinical validation data. Pertaining to validation data, where available, we recorded the number of patients or studies included, sensitivity, specificity, accuracy, and/or receiver operating characteristic area under the curve, along with information on ground-truthing of use-cases. Data were analyzed with pivot tables and charts for descriptive statistics and trends.
We noted an increasing number of FDA-regulated AI/ML from 2008 to 2021. Seventeen (17/118) regulated AI/ML algorithms posted no validation claims or data. Just 9/118 reviewed AI/ML algorithms had a validation dataset sizes of over 1000 patients. The most common type of AI/ML included image processing/quantification (IPQ; n = 59/118), and triage (CADt; n = 27/118). Brain, breast, and lungs dominated the targeted body regions of interest.
Insufficient public information on validation datasets in several FDA-regulated AI/ML algorithms makes it difficult to justify clinical applications since their generalizability and presence of bias cannot be inferred.
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, ...manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.
The Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 advances the goal of tying Medicare payments to quality and value. In April 2016, CMS published an initial proposed rule for MACRA, ...renaming it the Quality Payment Program (QPP). Under QPP, clinicians receive payments through either advanced alternative payment models or the Merit-Based Incentive Payment System (MIPS), a consolidation of existing federal performance programs that applies positive or negative adjustments to fee-for-service payments. Most physicians will participate in MIPS. This review highlights implications of the QPP and MIPS for radiologists. Although MIPS incorporates radiology-specific quality measures, radiologists will also be required to participate in other practice improvement activities, including patient engagement. Recognizing physicians' unique practice patterns, MIPS will provide special considerations in performance evaluation for physicians with limited face-to-face patient interaction. Although such considerations will affect radiologists' likelihood of success under QPP, many practitioners will be ineligible for the considerations under currently proposed criteria. Reporting using qualified clinical data registries will benefit radiologists' performance by allowing expanded arrays of MIPS and non-MIPS specialty-specific measures. A group practice reporting option will substantially reduce administrative burden but introduce new challenges by requiring uniform determination of patient-facing status and performance measurement for all of the group's physicians (diagnostic radiologists, interventional radiologists, and nonradiologists) under the same taxpayer identification number. Given that the initial MIPS performance period begins in 2017, radiologists must begin preparing for QPP and taking actions to ensure their future success under this new quality-based payment system.
The practice of the radiological sciences has always been dynamic. From economics and payment policy to imaging appropriateness, the ACR has led the way in keeping our specialty ahead of the curve. ...However, being ahead of the curve is a fragile place, and constant diligence is needed to remain there. There will always be major changes on our horizon, and the ACR will be there to empower us to adapt to change.
PDF
