Breaking Bias: The Role of Artificial Intelligence in Improving Clinical Decision-Making.

This case report reflects on a delayed diagnosis for a 27-year-old woman who reported chest pain and shortness of breath to the emergency department. The treating clinician reflects upon how cognitive biases influenced their diagnostic process and how multiple missed opportunities resulted in missteps. Using artificial intelligence (AI) tools for clinical decision-making, we suggest how AI could augment the clinician, and in this case, delayed diagnosis avoided. Incorporating AI tools into clinical decision-making brings potential benefits, including improved diagnostic accuracy and addressing human factors contributing to medical errors. For example, they may support a real-time interpretation of medical imaging and assist clinicians in generating a differential diagnosis in ensuring that critical diagnoses are considered. However, it is vital to be aware of the potential pitfalls associated with the use of AI, such as automation bias, input data quality issues, limited clinician training in interpreting AI methods, and the legal and ethical considerations associated with their use. The report draws attention to the utility of AI clinical decision-support tools in overcoming human cognitive biases. It also emphasizes the importance of clinicians developing skills needed to steward the adoption of AI tools in healthcare and serve as patient advocates, ensuring safe and effective use of health data.

Electrocardiogram-based biometrics for user identification - Using your heartbeat as a digital key.

External biometrics such as thumbprint and facial recognition have become standard tools for securing our digital devices and protecting our data. These systems, however, are potentially prone to copying and cybercrime access. Researchers have therefore explored internal biometrics, such as the electrical patterns within an electrocardiogram (ECG). The heart's electrical signals carry sufficient distinctiveness to allow the ECG to be used as an internal biometric for user authentication and identification. Using the ECG in this way has many potential advantages and limitations. This article reviews the history of ECG biometrics and explores some of the technical and security considerations. It also explores current and future uses of the ECG as an internal biometric.

Comparative study of a single lead ECG in a wearable device.

BACKGROUND: Technological advances have led to electrocardiograph (ECG) functionality becoming increasingly accessible in wearable health devices, which has the potential to vastly expand the clinician's ability to monitor, diagnose, and manage cardiac health conditions. However, achieving the high signal quality necessary to make an accurate and confident diagnosis is inherently challenging on consumer device-acquired ECGs. Effective signal conditioning is crucial to make ECG data from wearable devices clinically actionable. OBJECTIVE: This study evaluates the heart rate (HR) performance of ECG data collected on the HeartKey® Test Watch, a single lead, dry electrode wrist wearable, against data acquired on two criterion devices: the Bittium® Faros 180, a gold standard wet electrode ambulatory monitoring device, and the HeartKey Chest Module. METHODS: ECG data was simultaneously acquired on three devices during a multi-stage protocol (sitting, walking, standing) designed to reflect the motion noise of real-life scenarios. Raw ECGs from the HeartKey Test Watch and HeartKey Chest Module were processed through HeartKey software, and the accuracy of the outputted heart rate data was compared to that of the criterion device at each stage of the protocol. A beat rejection analysis was performed to provide insight into the degree of high-frequency noise present in ECGs recorded on the HeartKey Test Watch. RESULTS: Data acquired on the HeartKey Test Watch and processed by HeartKey software generated HR metrics that closely matched that of the criterion devices throughout the protocol. Bland-Altman analysis showed a mean absolute HR difference of 0.74, 1.21, 0.80 bpm during the sitting, walking, and standing stages respectively, which is within the ± 10% or ±5 bpm range required by ANSI EC13. ECG data from the HeartKey Test Watch had a higher beat rejection rate relative to the HeartKey Chest Module (8.5% vs ∼0%) due to the excessive high-frequency noise generated during the motion-based protocol. CONCLUSION: HeartKey software demonstrated highly accurate HR performance, comparable to that of the criterion Faros device, when processing challenging ECG data acquired on a single lead, dry electrode wrist wearable during both non-motion and motion-based protocols.

A universal, high-performance ECG signal processing engine to reduce clinical burden.

BACKGROUND: Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. OBJECTIVE: This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). METHODS: Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. RESULTS: HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. CONCLUSION: HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Virtual reality device to improve the tolerability of lumbar puncture.

Background: Virtual reality is increasingly being used as an adjunct or replacement to pharmacological analgesia and sedation during medical procedures. Methods and results: We report the successful use of a virtual reality device in a highly anxious patient undergoing lumbar puncture. Conclusion: The case demonstrates how virtual reality technology may benefit patients undergoing invasive procedures such as lumbar puncture. Virtual reality may, therefore, offer an alternative or adjunct to sedation and analgesia and may reduce the amount of pharmacological therapy required.