Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms.

BACKGROUND AND PURPOSE: Approaches to management of intracranial aneurysms are inconsistent, in part due to apprehension relating to potential malpractice claims. The purpose of this article was to review the causes of action underlying medical malpractice lawsuits related to the diagnosis and management of intracranial aneurysms and to identify the factors associated and their outcomes. MATERIALS AND METHODS: We consulted 2 large legal databases in the United States to search for cases in which there were jury awards and settlements related to the diagnosis and management of patients with intracranial aneurysms in the United States. Files were screened to include only those cases in which the cause of action involved negligence in the diagnosis and management of a patient with an intracranial aneurysm. RESULTS: Between 2000 and 2020, two hundred eighty-seven published case summaries were identified, of which 133 were eligible for inclusion in the analysis. Radiologists constituted 16% of 159 physicians sued in these lawsuits. Failure to diagnose was the most common medical malpractice claim referenced (100/133 cases), with the most common subgroups being "failure to include cerebral aneurysm as a differential and thus perform adequate work-up" (30 cases), and "failure to correctly interpret aneurysm evidence on CT or MR imaging" (16 cases). Only 6 of these 16 cases were adjudicated at trial, with 2 decided in favor of the plaintiff (awarded $4,000,000 and $43,000,000, respectively). CONCLUSIONS: Incorrect interpretation of imaging is relatively infrequent as a cause of malpractice litigation compared with failure to diagnose aneurysms in the clinical setting by neurosurgeons, emergency physicians, and primary care providers.

Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging.

AIM: To examine the current landscape of US Food and Drug Administration (FDA)-approved artificial intelligence (AI) medical imaging devices and identify trends in clinical validation strategy. MATERIALS AND METHODS: A retrospective study was conducted that analysed data extracted from the American College of Radiology (ACR) Data Science Institute AI Central database as of November 2021 to identify trends in FDA clearance of AI products related to medical imaging. Product and clinical validation information of each device was gathered from their respective public 510(k) summary or de novo request submission, depending on their type of authorisation. RESULTS: Overall, the database included a total of 151 AI algorithms that were cleared by the FDA between 2008 and November 2021. Out of the 151 FDA summaries reviewed, 97 (64.2%) reported the use of clinical data to validate their device, with six (4%) revealing study participant demographics, and eight (5.3%) reporting the specifications of the machines used. A total of 51 (33.8%) AI devices characterised their clinical data as multicentre, three (2%) as single-centre, and the remaining 97 (64.2%) did not specify. The ground truth used for clinical validation was specified in 78 (51.6%) FDA summaries. CONCLUSION: A wide breadth of AI algorithms has been developed for medical imaging. Most of the FDA summaries of the devices mention their use of clinical data and patient cases for device validation; however, few devices revealed the patient demographics or machine specifications used in their clinical studies, which may lead some consumers to question their external validation.