Expert witness blinding strategies to mitigate bias in radiology malpractice cases: a comprehensive review of the literature

The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis

Background

Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS.

Methods

This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations. The region of interest (ROI) was defined by two radiologists using a 3D Slicer open-source platform, utilizing blind analysis of sagittal CT images against histopathological examination results. PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. Machine learning algorithms were trained and evaluated using techniques like the area under the receiver operating characteristic curve (AUC; confusion matrix-related metrics, calibration plot, and decision curve analysis to assess their ability to differentiate PS from BS. Additionally, permutation feature importance (PFI; local interpretable model-agnostic explanations (LIME; and Shapley additive explanation (SHAP) techniques were utilized to gain insights into the interpretabilities of the models that are otherwise considered opaque black-boxes.

Results

A total of 15 radiomics features were screened during the analysis. The AUC value and Brier score of best the model were 0.88 and 0.13, respectively. The calibration plot and decision curve analysis displayed higher clinical efficiency in the differential diagnosis. According to the interpretation results, the most impactful features on the model output were wavelet LHL small dependence low gray-level emphasis (GLDN).

Conclusion

The CT-based radiomics models that we developed have proven to be useful in reliably differentiating between PS and BS at an early stage and can provide a reliable explanation for the classification results.

Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review

RATIONALE AND OBJECTIVE

Image interpretation is a fundamental aspect of radiology. The treatment and management of patients relies on accurate and timely imaging diagnosis. However, errors in radiological reports can negatively impact on patient health outcomes. These misdiagnoses can be caused by several different errors, but cognitive biases account for 74 % of all image interpretation errors. There are many biases that can impact on a radiologist's perception and cognitive processes. Several recent narrative reviews have discussed these cognitive biases and have offered possible strategies to mitigate their effects. However, these strategies remain untested. Therefore, the purpose of this scoping review is to evaluate the current knowledge on the extent that cognitive biases impact on medical image interpretation.

MATERIAL AND METHODS

Scopus and Medline Databases were searched using relevant keywords to identify papers published between 2012 and 2022. A subsequent hand search of the narrative reviews was also performed. All studies collected were screened and assessed against the inclusion and exclusion criteria.

RESULTS

Twenty-four publications were included and categorised into five main themes: satisfaction of search, availability bias, hindsight bias, framing bias and other biases. From these studies, there were mixed results regarding the impact of cognitive biases, highlighting the need for further investigation in this area. Moreover, the limited and untested debiasing methods offered by a minority of the publications and narrative reviews also suggests the need for further research. The potential of role of artificial intelligence is also highlighted to further assist radiologists in identifying and mitigating these cognitive biases.

CONCLUSION

Cognitive biases can impact radiologists' image interpretation, however the effectiveness of debiasing strategies remain largely untested.

Characteristics of Radiologists Serving as Medical Malpractice Expert Witnesses for Defense Versus Plaintiff

PURPOSE

Previous studies have reported higher qualification characteristics for anesthesiologists, neurosurgeons, orthopedic surgeons, and otolaryngologists serving as defense (versus plaintiff) medical malpractice expert witnesses. We assessed such characteristics for radiologist expert witnesses.

METHODS

Using the Westlaw legal research database, we identified radiologists serving as experts in all indexed medical malpractice cases between 2010 and 2019. Online databases were used to identify years of practice experience and scholarly bibliometrics. Using Medicare claims, individual radiologist practice types and mixes were ascertained. Radiologists testifying at least once each for defense and plaintiff were excluded from our defense-only versus plaintiff-only comparative analysis.

RESULTS

Initial Boolean searches yielded 1,042 potential cases; subsequent manual review identified 179 radiologists testifying in 231 lawsuits: 143 testified in one case (58 defense, 85 plaintiff) and 36 testified in multiple cases (10 defense-only, 14 plaintiff-only, 12 both). The 68 defense-only experts had fewer years of practice experience than the 99 plaintiff-only experts (28.3 versus 31.8 years, P = .02), but the two groups were otherwise similar in both practice type (44.6% versus 54.9% academic, P = .62) and mix (63.8% versus 65.8% practiced as subspecialists, P = .37) and as well as numbers of publications (60.5 versus 62.8, P = .86), citations (1,994.1 versus 2,309.2, P = .56), and h-indices (17.2 versus 16.8, P = .89).

CONCLUSIONS

In contrast to other specialists, radiologists serving as medical malpractice expert witnesses for defense and plaintiff display similar qualifications across various characteristics. Published practice parameter guidelines and experts' ability to blindly review archived original images might together explain this interspecialty discordance.

Artificial intelligence in medicine creates real risk management and litigation issues

The next step in the evolution of electronic medical record (EMR) use is the integration of artificial intelligence (AI) into health care. With the benefit of roughly 15 years of electronic medical records (EMR) data from millions of patients, health systems can now leverage this historical information via the assistance of complex mathematical algorithms to formulate computer-based medical decisions. With AI spending in health care forecasted to increase from $2.1 billion currently to $36 billion by 2025,¹ we sit on the precipice of the next revolution in health care. Now is the time to consider the potential risks, liability and litigation issues of using AI in health care.

The Effect of Visual Hindsight Bias on Radiologist Perception

RATIONALE AND OBJECTIVES

To measure the effect of visual hindsight bias on radiologists' perception during chest radiograph pulmonary nodule detection.

MATERIALS AND METHODS

This was a prospective multi-observer study to assess the effect of hindsight bias on radiologists' perception. Sixteen radiologists were asked to interpret 15 postero-anterior chest images containing a solitary lung nodule each consisting of 25 incremental levels of blur. Participants were requested initially to detect the nodule by reducing the blur of the images (foresight). They were then asked to increase the blur until the identified nodule was undetectable (hindsight). Participants then repeated the experiment, after being informed of the potential effects of hindsight bias and asked to counteract these effects. Participants were divided into two groups (experienced and less experienced) and the nodules were given different conspicuity ratings to determine the effect of expertise and task difficulty. Eye tracking technology was also utilised to capture visual search.

RESULTS

Wilcoxon analysis demonstrated significant differences between foresight and hindsight values of the radiologists (p = 0.02). However, after being informed of hindsight bias, these differences were no longer significant (p = 0.97). Friedman analysis also determined overall significance in the hindsight ratios between nodule conspicuities for both phases (phase 1: p = 0.02; phase 2: p = 0.02). There was no significance difference between the experienced and less experienced groups.

CONCLUSION

This study demonstrated that radiologists exhibit hindsight bias but appeared to be able to compensate for this phenomenon once its effects were considered. Also, visual hindsight bias appears to be affected by task difficulty with a greater effect occurring with less conspicuous nodules.

Medicolegal training in radiology; an overlooked component of the non-interpretive skills curriculum

OBJECTIVE

Radiologists comprise approximately 3.6% of US physicians while ranked 6th-8th in medicolegal claims. Studies suggest that by the age of 60, about half of all radiologists will be sued at least once. Given this likelihood, it is surprising how little attention is paid to teaching of medicolegal issues. It is hypothesized that most trainees emerge from residency with only a vague notion of the medicolegal issues inherent in radiology.

METHODS

All of the radiology attendings, trainees and alumni in our tertiary care teaching hospital were surveyed via an electronic questionnaire. Respondents were surveyed on overall knowledge of job-related medicolegal issues and willingness to receive additional education. The survey also included two real life medicolegal scenarios and the radiologists were asked to choose the most likely outcome.

RESULTS

A questionnaire was sent to total of 359 trainees, attendings and alumni. There were 168 responses, constituting a 46.7% response rate, F:M 48:112. Only 41% of the respondents were aware that by the age of 60, half of them would be involved in at least one lawsuit. All knew the most common causes of malpractice claims; however, one-fourth were not aware of available medicolegal resources offered by radiological organizations; 85% of the respondents expressed willingness to attend medicolegal CME courses. All residents surveyed believed that medicolegal lectures should be included in their didactics.

CONCLUSION

There is a dearth of knowledge among radiologists on job-related medicolegal topics. This survey suggests that incorporating additional medicolegal topics into the non-interpretive skills curriculum of residents and medicolegal CME for graduates would be well received.

Redefining the Practice of Peer Review Through Intelligent Automation Part 1: Creation of a Standardized Methodology and Referenceable Database

Conventional peer review practice is compromised by a number of well-documented biases, which in turn limit standard of care analysis, which is fundamental to determination of medical malpractice. In addition to these intrinsic biases, other existing deficiencies exist in current peer review including the lack of standardization, objectivity, retrospective practice, and automation. An alternative model to address these deficiencies would be one which is completely blinded to the peer reviewer, requires independent reporting from both parties, utilizes automated data mining techniques for neutral and objective report analysis, and provides data reconciliation for resolution of finding-specific report differences. If properly implemented, this peer review model could result in creation of a standardized referenceable peer review database which could further assist in customizable education, technology refinement, and implementation of real-time context and user-specific decision support.

Redefining the Practice of Peer Review Through Intelligent Automation-Part 3: Automated Report Analysis and Data Reconciliation

One method for addressing existing peer review limitations is the assignment of peer review cases on a completely blinded basis, in which the peer reviewer would create an independent report which can then be cross-referenced with the primary reader report of record. By leveraging existing computerized data mining techniques, one could in theory automate and objectify the process of report data extraction, classification, and analysis, while reducing time and resource requirements intrinsic to manual peer review report analysis. Once inter-report analysis has been performed, resulting inter-report discrepancies can be presented to the radiologist of record for review, along with the option to directly communicate with the peer reviewer through an electronic data reconciliation tool aimed at collaboratively resolving inter-report discrepancies and improving report accuracy. All associated report and reconciled data could in turn be recorded in a referenceable peer review database, which provides opportunity for context and user-specific education and decision support.

Non-contrast MR angiography using three-dimensional balanced steady-state free-precession imaging for evaluation of stenosis in the celiac trunk and superior mesenteric artery: a preliminary comparative study with computed tomography angiography

OBJECTIVE

Although non-contrast MR angiography (NC-MRA) is well established for the evaluation of renal artery stenosis, its usefulness in the evaluation of other abdominal aortic branches remains to be studied. This study aimed at evaluating the image quality and diagnostic accuracy of NC-MRA using a three-dimensional balanced steady-state free-precession sequence in identifying stenosis in the celiac trunk (CTR) and superior mesenteric artery (SMA) as compared with CT angiography (CTA) as the reference standard.

METHODS

41 patients underwent both NC-MRA and CTA of the abdominal aorta. Two radiologists analyzed the quality of the images (diagnostic vs non-diagnostic) and the performance (accuracy, sensitivity and specificity) of NC-MRA for the identification of arterial stenosis. Kappa tests were used to determine the interobserver agreement and the intermethod agreement between NC-MRA and CTA.

RESULTS

NC-MRA provided diagnostic quality images of the CTR and SMA in 87.8% and 90.2% of cases, respectively, with high interobserver agreement (kappa 0.95 and 0.80, respectively). For stenosis assessment, NC-MRA had a sensitivity of 100%, a positive-predictive value of 50% and a negative-predictive value of 100% for both segments, with accuracies of 88.8% for the CTR and 94.5% for the SMA.

CONCLUSION

NC-MRA is an accurate method for detecting stenosis in the CTR and SMA. Advances in knowledge: Data from this study suggest that MR angiography with balanced steady-state free-precession sequence is a viable non-contrast alternative for stenosis evaluation of these branches in patients for whom a contrast-enhanced examination is contraindicated.

Outcomes Knowledge May Bias Radiological Decision-making

RATIONALE AND OBJECTIVES

This research investigates whether an expectation of abnormality and prior knowledge might potentially influence the decision-making of radiologists, and discusses the implications for radiological expert witness testimony.

MATERIALS AND METHODS

This study was a web-based perception experiment. A total of 12 board-certified radiologists were asked to interpret 40 adult chest images (20 abnormal) twice and decide if pulmonary lesions were present. Before the first viewing, a general clinical history was given for all images: cough for 3+ weeks. This was called the "defendants read." Two weeks later, the radiologists were asked to view the same dataset (unaware that the dataset was unchanged). For this reading, the radiologists were given the following information for all images: "These images were reported normal but all of these patients have a lung tumour diagnosed on a subsequent radiograph 6 months later." They were also given the lobar location of the newly diagnosed tumor. This was called the "expert witness read."

RESULTS

There was a significant difference in location-based sensitivity (W = -45, P = 0.02) between the two conditions with nodule detection increasing under the second condition. Specificity increased outside the lobe of interest (W = 727, P = < 0.0001) and decreased within the lobe of interest (W = -237, P = 0.03) significantly in the "expert witness" read. Case-based sensitivity and case-based specificity were unaffected.

CONCLUSIONS

This study showed evidence that increased clinical information affects the performance of radiologists. This effect may bias expert witnesses in radiological malpractice litigation.