AI Medical Compendium Topic

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Improved bioimpedance spectroscopy tissue classification through data augmentation from generative adversarial networks.

Medical & biological engineering & computing
Bioimpedance spectroscopy is a tissue classification technique with many clinical applications. Similarly to other data-driven methods, it requires large amounts of data to accurately distinguish similar classes of tissue. Classifiers trained on smal...

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities.

Sensors (Basel, Switzerland)
In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient ass...

Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference.

The Journal of allergy and clinical immunology
Here, we summarize the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies conference, during which experts and advocates gathered to advance research into the applications of artificial intelligence (AI), machine lear...

Federated clustered multi-domain learning for health monitoring.

Scientific reports
Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient car...

Implementation of a Clinical, Patient-Level Dashboard at a Mental Health Hospital: Lessons Learned from Two Pilot Clinics.

Studies in health technology and informatics
The Centre for Addiction and Mental Health has implemented mechanisms to standardize routine data collection with the vision of a Learning Health System. To improve clinical decision-making and patient outcomes, a clinical dashboard was implemented t...

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges.

Journal of medical Internet research
As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as genera...

Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.

JCO clinical cancer informatics
This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions.

Integrative and comparative biology
In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and ...

Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.

American journal of epidemiology
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou a...

Weighing the benefits and risks of collecting race and ethnicity data in clinical settings for medical artificial intelligence.

The Lancet. Digital health
Many countries around the world do not collect race and ethnicity data in clinical settings. Without such identified data, it is difficult to identify biases in the training data or output of a given artificial intelligence (AI) algorithm, and to wor...