AIMC Topic: Military Personnel

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Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Translational psychiatry
Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre...

Machine learning for classifying chronic ankle instability based on ankle strength, range of motion, postural control and anatomical deformities in delivery service workers with a history of lateral ankle sprains.

Musculoskeletal science & practice
OBJECTIVE: Chronic ankle instability (CAI) frequently develops as a result of lateral ankle sprains (LAS) in delivery service workers (DSWs). Identifying risk factors for CAI is crucial for implementing targeted interventions. This study aimed to dev...

Application of machine learning in predicting health perception through military personnel's sense of empowerment.

Applied psychology. Health and well-being
The promotion of health and provision of care services for new recruits are issues of constant concern for military leaders and healthcare providers, as they are crucial to maintaining and operating military forces. The enhancement of military person...

Predicting special forces dropout via explainable machine learning.

European journal of sport science
Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or no...

Future of service member monitoring: the intersection of biology, wearables and artificial intelligence.

BMJ military health
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed...

Distributing Blame Among Multiple Entities When Autonomous Technologies Cause Harm.

Personality & social psychology bulletin
As autonomous technology emerges, new variations in old questions arise. When autonomous technologies cause harm, who is to blame? The current studies compare reactions toward harms caused by human-controlled vehicles (HCVs) or human soldiers (HSs) t...

Design and testing of ultrasound probe adapters for a robotic imaging platform.

Scientific reports
Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical...

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

American journal of preventive medicine
INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.

Responsible use of AI in military systems: prospects and challenges.

Ergonomics
Artificial Intelligence (AI) holds great potential for the military domain but is also seen as prone to data bias and lacking transparency and explainability. In order to advance the trustworthiness of AI-enabled systems, a dynamic approach to the de...

Lay representations of artificial intelligence and autonomous military machines.

Public understanding of science (Bristol, England)
This study is about how lay persons perceive and represent artificial intelligence in general as well as its use in weaponised autonomous ground vehicles in the military context. We analysed the discourse of six focus groups in Estonia, using an auto...