Virtual reality interventions for victims of crime: A systematic review.

In the forensic field, most studies employing virtual reality (VR) interventions have focused on offenders. The validity and safety of VR applications for victims of crime are still unclear. Following PRISMA guidelines, a systematic review on VR interventions for crime victims was performed to assess the efficacy, acceptability by patients, and cost-effectiveness of these interventions compared to in-person care. We identified 34 potentially eligible studies from 188 records obtained from database searches (Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus); four additional articles were identified via alternative sources. In total, nine articles were included for the qualitative synthesis. Patient satisfaction with VR interventions was found to be equivalent to face-to-face interventions. Both VR exposure and control groups found relief from posttraumatic symptoms, with differences either statistically insignificant or in favor of VR. Despite the increased costs linked to the technology required, VR appears to be a promising alternative to in vivo exposure, but further research is needed. Limitations of the review include the varied experimental protocols, which did not allow us to conduct a quantitative analysis and comparison of findings across different studies, and the generally poor quality of the studies included. Further research, preferably in larger groups, is needed to shed more light on the effectiveness of VR interventions for traumatized victims of crime.

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry.

The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective.

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.