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Out of their minds? Externalist challenges for using AI in forensic psychiatry

Publications associées (40)

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Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri

Integrating smart algorithms on neural devices presents significant opportunities for various brain disorders. In this paper, we review the latest advancements in the development of three categories of intelligent neural prostheses featuring embedded signa ...

arXiv2024

Adolescent Stress-Induced Ventral Hippocampus Redox Dysregulation Underlies Behavioral Deficits and Excitatory/Inhibitory Imbalance Related to Schizophrenia

Maria del Carmen Sandi Perez, Dogukan Hazar Ülgen, Thamyris Silva

Background and hypothesis: Redox dysregulation has been proposed as a convergent point of childhood trauma and the emergence of psychiatric disorders, such as schizophrenia (SCZ). A critical region particularly vulnerable to environmental insults during ad ...

2024

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

2024

A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC

Mahsa Shoaran, Uisub Shin, Cong Ding

Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter therefore presents a low-power SoC with both neural connectivity extraction ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

20-years work investigating the shine-through paradigm in the schizophrenia spectrum: what is next?

Michael Herzog, Simona Adele Garobbio, Maya Roinishvili

In the past 20 years, our laboratory has proved the shine-through paradigm to be a very sensitive endophenotype for schizophrenia. The shine-through paradigm is a visual backward masking task, where the target is a vertical vernier followed by a 25-element ...

2023

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

2023

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Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...

2023

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Towards more Practical Threat Models in Artificial Intelligence Security

Alexandre Massoud Alahi, Kathrin Grosse

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

arXiv2023

Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma a ...

2022