Diagnosing psychiatric conditions that involve the intentional or unconscious production of symptoms remains a significant challenge in clinical practice. This presentation examines a clinical case that highlights the difficulties in distinguishing between Factitious Disorder, Conversion Disorder, and Simulation. A 34-year-old woman was admitted with sudden onset of neurological symptoms, including pseudo epileptic crises. Her symptoms fluctuated inconsistently with clinical observation and failed to correlate with established neurological patterns, raising the suspicion of Conversion Disorder. However, further investigation revealed inconsistencies in her medical history and a pattern of seeking unnecessary treatments, suggesting the possibility of Factitious Disorder. Additionally, external incentives, such as the potential for financial compensation, prompted consideration of Simulation. The case presents a diagnostic dilemma that underscores the overlapping features of these conditions.

Neuroimaging may provide valuable insights into the case, supporting the exclusion of neurological pathologies but also subtle changes in brain activity in the areas involved in emotional regulation and self-representation, which may suggest the involvement of underlying psychological factors common in both Conversion and Factitious Disorder.

This case exemplifies the critical nosological challenges in differentiating between Factitious Disorder, Conversion Disorder, and Simulation. It highlights the importance of a comprehensive clinical approach, including neuroimaging, thorough psychological assessment, and consideration of psychosocial factors. The discussion aims to deepen understanding of these complex disorders and promote more accurate and nuanced diagnostic practices in psychiatry.

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The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a performance-based measure developed to assess functional capacity through simulations of daily activities. This study examined its psychometric properties in a Spanish sample, including individuals with first-episode psychosis (FEP), schizophrenia, and healthy controls.

A total of 370 participants (99 FEP, 116 schizophrenia, and 155 controls) completed the VRFCAT in a multicenter study. Internal consistency (McDonald’s omega), discriminative validity (group comparisons and ROC curves), and convergent validity via correlations with cognitive performance and clinical symptoms were examined. Reference percentiles were calculated from the healthy control sample using quantile regression, stratified by age and education.

Item-level VRFCAT completion times showed acceptable to good internal consistency overall and in controls and schizophrenia samples, but poor in FEP. Differences in VRFCAT performance emerged (χ2 = 108.88, p < .001), with controls performing best, schizophrenia worst, and FEP in between. ROC analyses indicated good discriminative accuracy in distinguishing patients from controls (area under the curve [AUC] = 0.779, sensitivity = 80.0%, specificity = 64.2%); but limited discrimination between schizophrenia and FEP. Age and education, but not sex, significantly affected performance. VRFCAT showed small-to-moderate correlations with cognitive performance, and no significant associations with symptom severity.

The VRFCAT is a reliable and valid tool for assessing functional capacity in Spanish-speaking individuals with psychotic disorders. Its ecological validity, objectivity, psychometric properties, brief administration time, and ease of use support its potential use in clinical and research settings for evaluating functional recovery and treatment outcomes.

The recent years witnessed an increase in the knowledge regarding autoimmune encephalitis (AE). These autoimmune entities often present with mixed psychiatric and neurologic features and in up to 4% of the cases the presentation is purely psychiatric. The diagnosis can be made only through the discovery of Neuronal Surface Autoantibodies (NSAbs) in the Cerebro Spinal Fluid, but symptoms and signs of possible and probable diagnosis have been described (Pollack et al., 2020). However, NSAbs can be found also in peripheral blood in various percentage of patients. The role of these antibodies in psychiatric patients is yet not known.

The PHLAMES study aims at evaluating first episode psychosis (FEP) patients for signs and symptoms of AE in a psychiatric setting, with the double objective of assessing the diagnosis of AE and the role of circulating NSAbs in psychiatric patients, through clinical evaluation, biological samples, and neuroimaging.

In the PHLAMES study, all patients with FEP (<6 months from the onset) were tested with a diagnostic algorithm for signs or symptoms of AE. A complete psychiatric and neurologic assessment was performed; cognitive tests were administered. All patients underwent blood sample to test for circulating auto-antibodies against SNC structures.

A subsample also underwent MRI, including gadolinium contrast.

Analyses compared patients testing positive for serum NSAbs (NSAbs-POS) to those tested negative (NSAbs-Neg).

12.8% of the patients tested positive for serum NSAbs (NSAbs-POS). No difference in terms of age, sex, BMI, years od education, and ethnicity was found between groups.

Regarding the neurologic variables, NSAbs-POS significantly showed more memory deficits, parkinsonism signs, and speech disorders (p<0.001), compared to NSAbs-NEG patients. Similarly, NSAbs-POS patients presented a significant in TMT-A, Raven, and RAVLT scores (p<0.05) compared to NSAbs-NEG. Finally, NSAbs-POS patients presented an increased score at PANSS “Somatic implication” item and a reduced score at PANSS “Insight” (p<0.05) items, suggesting a higher concern of these patients regarding their help and a greater awareness of their condition.

Regarding MRI, differences are present between groups, both on structural and on contrasted images.

Our preliminary findings suggest the possibility that NSAbs-POS patients might represent a subpopulation of FEP with specific characteristics. These results are preliminary and need a confirmation in bigger samples, but they might represent a step towards the identification of clinically meaningful subgroups in FEP defined through an easy and not invasive test, helping to dissect the heterogeneity of psychiatric disorders and moving towards precision psychiatry.

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Suicide involves an act of volition on the part of the deceased, making it unlike deaths from physical disorders such as cancer or stroke. The latter occur passively and often despite the efforts of the patient to stay alive. Yet when there is a suicide, clinicians involved may often be blamed and families may often feel guilt. This contrasts with the default response of praise and thanks to clinicians following treatment preceding deaths from physical disorders.

Comparative standardized mortality rate (SMR) data are analyzed to demonstrate the impact of developments in care over the past two decades in the United Kingdom (UK), and similar United States (USA) SMR data are noted. The evidence is reviewed regarding our ability to predict who will die by suicide, when and where to target intervention, and practical and effective prevention methods.

Data from the UK are presented that reflects the relative lack of impact of prevention efforts on suicide mortality rates when compared to the reductions seen in various physical disorders. This narrative review comments on the causes and consequences of this difference.

The challenge for psychiatry is that SMR data suggest that we have been unable to significantly reduce suicide SMR unlike that for physical disorders. This needs to be fully acknowledged and the biased assumption of blame needs to stop. The focus needs to be on evidence-based interventions that do work, such as medications, psychological treatments, psychological interventions, and suicide prevention research.

The differentiation between somatic and psychiatric disorders presents a significant challenge in clinical practice due to overlapping symptomatology and complex etiologies. This abstract proposes an integrated approach utilizing laboratory tests and neuroimaging techniques to enhance diagnostic accuracy and improve patient outcomes. Somatic disorders, characterized by physical symptoms with identifiable organic causes, often mimic psychiatric conditions, leading to misdiagnosis and inappropriate treatment. Laboratory evaluations play a crucial role in ruling out medical conditions that may present with psychiatric-like symptoms. Thyroid function tests, vitamin B12 level assessments, and screening for infections are vital in this process. Routine blood tests further aid in detecting systemic conditions contributing to psychiatric presentations. Neuroimaging techniques offer visual insights into brain structure and function, facilitating the differentiation between psychiatric and neurological conditions. Structural imaging modalities like MRI and CT scans can reveal abnormalities such as tumors or lesions. Functional imaging, including PET and SPECT scans, assesses cerebral metabolism and blood flow, identifying anomalies associated with specific psychiatric disorders. Integrating laboratory tests with neuroimaging significantly enhances diagnostic precision. In suspected dementia cases, for instance, laboratory tests can exclude metabolic or infectious causes, while neuroimaging can identify characteristic patterns of Alzheimer’s disease or vascular dementia. This combined approach ensures a thorough evaluation, reducing misdiagnosis and facilitating targeted treatment strategies. Clinical studies have demonstrated the efficacy of this integrated approach in improving diagnostic accuracy for conditions like major depressive disorder and bipolar disorder. Functional neuroimaging has also proven instrumental in distinguishing between psychiatric disorders and neurological conditions such as epilepsy. Despite its advantages, this approach faces challenges, including cost, availability, and the need for standardized interpretation protocols. Future directions include advancements in neuroimaging accessibility, research into novel biomarkers, and the integration of artificial intelligence for enhanced diagnostic accuracy and personalized treatment planning. In conclusion, integrating laboratory tests with neuroimaging represents a significant advancement in the differential diagnosis of somatic and psychiatric disorders. This comprehensive approach facilitates accurate diagnosis, ensuring patients receive appropriate and effective treatment, ultimately leading to improved patient outcomes.

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Understanding how suicide rates vary across age, sex, and geography is essential to designing effective prevention strategies. We examined long-term trends in suicide mortality across European countries over three decades, with a focus on age-specific trajectories.

Using the WHO mortality database, we computed annual sex- and age-specific suicide rates (10–14 to 85+ age groups) from 1990 to 2022, for the most populous European countries, and aggregated rates for the EU-27 and four geographical areas (North, West, South, and Centre-East Europe). We also calculated percentage differences across four time periods (1990–1994, 2000–2004, 2010–2014, and 2020–2022), according to data availability.

Suicide rates increased with age, peaking in older individuals (85+) in most countries (e.g., 82.0/100,000 in France in 2020–2022, 77.1/100,000 in Germany among males, in 2020), except in the UK and Northern Europe, where rates peaked at middle age (∼22/100,000 at 45–49, in 2020). EU-27 suicide rates in 2020 ranged from 5.5/100,000 (age 15–19) to 58.2/100,000 (85+) among males, and from 2.6 (15–19) to 8.6/100,000 (85+) among females. Male suicide rates were 3 to 8 times higher than female rates across all ages. While overall rates declined since 1990 in most countries, youth suicide increased after 2010 in Western (e.g., +12%, girls 15–19), Southern (+24.5%, girls 15–19), and Northern (+44%, girls 15–19 and 20–24) Europe. Rates among young and middle-aged adults recently rose in Spain, the UK, and Northern Europe, while they declined in Eastern Europe after the 1990s.

Despite overall declines, our findings highlight marked heterogeneity in sex- and age-specific trends in suicide mortality across Europe. These patterns call for age-tailored prevention strategies that address evolving psychosocial stressors and structural determinants across the lifespan.

Artificial Intelligence (AI) has evolved from philosophical musings to a crucial component of modern technology. The concept of using computer technology to simulate thinking and intelligent behavior was first described by Alan Turing in 1950. The Dartmouth Conference in 1956 marked a pivotal moment, coining the term “artificial intelligence” and fostering interdisciplinary collaboration. Further contributions to the development of AI theory were made by scholars such as John McCarthy, Valerie Barr, Edward Feigenbaum, Claude Shannon and others. The concept of creating AI is to form a system that would be able to operate autonomously, solving intellectual tasks in a manner similar to human cognitive processes. This is achieved by using algorithms that are translated into computer code containing instructions for quickly analyzing and transforming data into conclusions, information, or other outputs. Throughout the subsequent decades, AI experienced cycles of optimism and disillusionment, known as “AI winters,” primarily due to unmet expectations regarding computational capabilities and practical applications. The resurgence of AI in the 21st century can be attributed to breakthroughs in machine learning, especially deep learning, and the availability of vast datasets and increased computational power. Today, AI is defined broadly as the capability of a machine to imitate intelligent human behavior, encompassing a range of subfields including natural language processing, robotics, and computer vision. As AI continues to evolve, its definitions are adapted to address ethical considerations and the implications of autonomy in decision-making systems. Current discourse often examines AI not only as a technological phenomenon but also as a sociocultural force, raising questions about accountability, bias, and the future of work. This presentation will reflect the complex path of AI development and the shifting paradigms that define its scope and impact on modern society.

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Major depressive disorder (MDD) exhibits significant heterogeneity in treatment responses, necessitating multiple pharmacological trials to achieve therapeutic success. Pharmacogenetic (PGx) testing has emerged as a promising tool to personalize antidepressant (AD) treatments, though its clinical utility remains controversial.

This study assessed the efficacy of PGx-guided treatment in improving clinical outcomes among 287 MDD patients within the PANDORA trial, a prospective randomized, participant- and rater-blinded, controlled trial conducted in Italy. A total of 268 adults with moderate-to-severe MDD were randomized into Treated as Usual (TAU) or Treated with Genetic Test Guide (TGTG). Patients were assessed using the Hamilton Depression Rating Scale (HAM-D17), Beck Depression Inventory II (BDI-II), Beck Anxiety Inventory, MINI-ICF-APP for psychosocial functioning, and the UKU Side Effects Rating Scale, at baseline and at 4, 8, and 12 weeks.

Both groups demonstrated significant symptom improvement over the 12-week period. No significant differences were observed between the groups in terms of response and remission rates, measured by HAM-D17 and BDI-II, at weeks 8 and 12. Notably, in the BDI-II symptom cluster analysis, significant differences were found only in neurovegetative symptoms, with TGTG patients showing greater improvement at the 4-week and 8-week follow-up visits. Among patients with severe baseline symptoms, those in the TGTG group exhibited greater symptom reduction and higher response rates at week 8.

These findings suggest that while PGx testing did not significantly improve overall treatment efficacy in MDD compared to TAU, it may offer benefits in managing patients with severe symptoms and specific symptom domains.

Artificial intelligence (AI) is reshaping mental health care by improving diagnosis, treatment, and patient support. AI-driven tools streamline administrative tasks, enhance clinical documentation, contribute to medical education, and enable continuous symptom monitoring. Additionally, AI augments text-based support programs, offering accessible psychological interventions. However, while AI applications have been assessed for perceived empathy, their impact on clinical outcomes remains uncertain, highlighting the need for evidence-based implementation.

AI can immediately enhance efficiency in routine clinical tasks. Automation optimizes billing processes and reduces clerical burdens, allowing clinicians to focus more on patient care. In clinical documentation, AI-powered transcription and natural language processing (NLP) help generate structured medical records. AI also supports medical education by offering adaptive learning, personalized training, and real-time feedback through large-scale data analysis.

Beyond administrative support, AI plays a role in patient monitoring and early intervention. AI algorithms analyze speech, facial expressions, and behavioral data from smartphones and wearables, detecting mood fluctuations and early psychiatric symptoms. This real-time analysis can facilitate timely interventions and improve overall mental health care. Additionally, AI-powered chatbots and virtual therapists are increasingly used in digital mental health services, providing immediate, text-based psychological support. However, rigorous studies are needed to assess their effectiveness in improving clinical outcomes. A well-established framework for technology evaluation in mental health highlights five key areas for development: equity, privacy, evidence, clinical engagement, and interoperability. Addressing these factors is crucial to ensuring AI-driven solutions are accessible, secure, scientifically validated, clinically integrated, and capable of working across diverse health systems. By prioritizing these advancements, AI can move from theoretical promise to practical application, meaningfully improving mental health care delivery.

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There is a potential for new technologies in mental health and psychiatry. Artificial intelligence enables the design models that categorize different groups and predict different prognosis trajectories.

Natural language processing enables to use classical text data from electronic health records, for instance, to detect suicide trends and their risk factors (Bey et al., 2024). This opens new perspectives in the analysis of large Electronic Health Records databases.

Artificial intelligence can extract and combine new features, like posture, physiological signal of stress and facial expression. This could be particularly important to bypass insight development in children of adolescents (Bourvis et al., 2021) while taking the opportunity of early management. This could help to optimize exposure therapy (Mahmoudi-Nejad et al., 2024), detect tantrums in non-verbal children (Cano et al., 2024) or even improve motivation for physical activity (Nuss et al., 2020).

In motion assessment, we could detect motor assessment difficulties in children with autism from typical counterparts (Gargot et al., 2022). We can also automatically detect writing difficulties (Agarwal et al, 2023).

However, AI struggles with an interpretability problem (black box). Their model are complex, the features extracted are not always obvious (Minh et al, 2021 ; Linardatos et al, 2020) .

Fine motor skills classic signal processing allows to tailor specific exercises to reeducate children with writing difficulties (Gargot et al., 2021).

Digital psychiatry however is impeded by poor user experience (Witteman et al., 2011), complex market models (Gollier-Briant et al., 2024).

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Artificial intelligence (AI) is transforming psychiatric training and education by enhancing diagnostic accuracy, improving therapeutic decision-making, and personalizing learning experiences for trainees. AI-driven simulations, virtual patients, and natural language processing (NLP)-based assessments allow for more effective skill development in psychiatric diagnosis and psychotherapy. Machine learning models provide evidence-based guidance, reinforcing clinical reasoning and treatment strategies. Ethical considerations, including patient confidentiality and bias mitigation, remain central to AI implementation in training. This session explores the latest advancements in AI-driven psychiatric education, discussing practical applications, challenges, and future directions for integrating AI into clinical training programs.

AI, psychiatry, education, machine learning, clinical training

1. Ahmed, M., & Rush, A. J. (2023). Artificial intelligence in psychiatry: Current applications and future directions. Journal of Psychiatric Research, 157, 106-121.

2. Ryu, S., & Kim, H. (2022). AI-based learning tools for medical education: A systematic review. Medical Teacher, 44(5), 512-520.

3. Luxton, D. D. (2021). Ethical implications of AI in mental health care. Journal of Ethics in Mental Health, 12, 1-14.

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In this paper several issues about cultural competence will be addressed, such as assumptions, Mason’s five progressive steps and learning needs of professionals. The notion of cultural competence combines an understanding of different belief systems, good communication skills (including highly specialist skills such as the communication of internal emotional states, and the cultural adaptation of treatment models and therapies. Professionals can measure theircompetence on a continuum developed by James Mason. His five progressive steps are: cultural destructiveness, incapacity, blindness, pre-competence, and competence. Next, the mental health needs of refugees will be discussed, especially for those who are at risk to become violent offenders. For example, some types of environmental and psychosocial stressors that refugees may experience day-to-day. Some of the cultural and attitudinal factors should be taken into account when working with refugees and wider communities. Finally, educational needs for trainees in (forensic) psychiatry and (forensic) psychiatrists will be highlighted. Knowledge about culture, ethnicity, race, religion, and identity is hereby crucial. Reflections will be made on the presented case.

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For women navigating both ADHD and the (peri)menopausal transition, sleep problems are a common and significant hurdle. This presentation explores effective ways to improve sleep for (peri)menopausal women with ADHD, drawing on research into hormonal, non-hormonal, and behavioural approaches. ADHD can make sleep difficult, contributing to issues like delayed sleep phase, insomnia, and restless legs syndrome/periodic limb movement disorder (RLS/PLMD), with roughly 60% of adults with ADHD screening positive for a sleep disorder. Research indicates that adults with ADHD often take longer to fall asleep and experience more sleep-related challenges than those without ADHD. Similarly, (peri)menopausal women often struggle with sleep disturbances, including poor sleep quality and increased restless legs symptoms. Considering that (peri)menopausal symptoms can also impact cognitive function, addressing these symptoms may lead to better sleep. Menopausal Replacement Therapy (MRT) may be beneficial, especially for women with vasomotor symptoms, as it can improve sleep quality and reduce nighttime awakenings. In addition, multiple studies suggest that bioidentical progesterone can improve sleep quality in perimenopausal women. We will also discuss non-hormonal options and behavioural strategies like cognitive behavioural therapy, exercise, and mindfulness techniques. With a significant percentage of women with ADHD diagnosed with a sleep disorder and prescribed sleep medication, and a similar percentage of perimenopausal women experiencing sleep disturbances, a personalised, integrated approach is key. This includes fine-tuning ADHD medication, managing any co-existing mood issues, and customising treatments to fit individual needs.

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Artifical Intelligecne is sold as a magic solution, and yet its inscrutability poses a major problem. Who is the source of the knowledge? Who is accountable? Who is responsible? Who can edit or train it? Has it been trained on copyrighted material? If so, have the owners been compensated? If not, is it culturally relevant? Has the ‘black box’ aspect been addressed? Whether a provider of knowledge, a clinical decison aid or a decsion maker, troubling issues arise that as yet have not been solved by legisaltion or the market. We shall explore potential benefits, solutions, and actual problems with real world cases that shed light on where AI may take us, and where we may need constarints that lie beyond medical ethics.

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Attention-Deficit/Hyperactivity Disorder (ADHD) In my talk, I will present the latest evidence, mainly based on previous meta-analyses, network meta-analyses, dose-response meta-analyses, and umbrella reviews, that can inform clinical decision-making in the field of pharmacological treatment for ADHD, including the choice of initial medication, titration, treatment of stimulant-refractory cases, management of cases with comorbid conditions, and management of adverse event.

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Early onset psychosis and, especially, early-onset schizophrenia (EOS), defined as the onset of psychotic symptoms before the age of 18, represent severe and frequently disabling conditions with adverse long-term functional consequences. Effective pharmacological treatment is critical to mitigating symptom progression, reducing relapse, and improving long-term outcomes. This presentation provides an update on current evidence-based pharmacological strategies for EOS, with a focus on efficacy, safety, and emerging treatments.

Second-generation antipsychotics (SGAs), such as aripiprazole, quetiapine, paliperidone, and risperidone, remain the mainstay of treatment, with lurasidone and brexpiprazole showing positive results in more recent studies. While olanzapine is clearly an effective SGA, its more pronounced cardiometabolic side effects have relegated olanzapine more to a second-line antipsychotic when other SGAs are ineffective. Thus, comparative efficacy, side effect profiles, and long-term metabolic risks are relevant when choosing among individual agents. The role of clozapine for treatment-resistant EOS and considerations regarding polypharmacy are relevant, given that EOS is one of the most reliable risk factors for treatment-resistant schizophrenia .

Long-acting injectable antipsychotics (LAIs) have not been explored in randomized trials so far, but are an important treatment tool, given the widespread non-adherence risk, which is even higher earlier in the illness. Novel mechanisms, including cholinergic muscarinic agonist treatments, recently approved for the first time for adults with schizophrenia, which address presynaptic hyperdopaminergia and in a highly selective fashion, need to be explored in EOS in the future. Additionally, the importance of early intervention strategies and adjunctive nonpharmacological and pharmacological treatments, e.g., mood stabilizers, antidepressants for specific domains of schizophrenia and for comorbid conditions, as well as best practices for transition into the adult psychiatry sector require further study.

In summary, treatment selection for youth with EOS should balance short-term as well as long-term efficacy considerations and safety concerns. While pharmacological advancements in EOS generally lag behind advances in adults, innovations are hoped to also reach EOS. More individualized and measurement-based approaches needed to be explored in both research settings and clinical care aiming at optimizing the pharmacological management for individuals living with EOS.

C. Correll Grant / Research support from: Boehringer-Ingelheim, Janssen and Takeda, Consultant of: AbbVie, Alkermes, Allergan, Angelini, Aristo, Autobahn, Boehringer-Ingelheim, Bristol-Meyers Squibb, Cardio Diagnostics, Cerevel, CNX Therapeutics, Compass Pathways, Darnitsa, Delpor, Denovo, Draig, Eli Lilly, Eumentis Therapeutics, Gedeon Richter, GH, Hikma, Holmusk, IntraCellular Therapies, Jamjoom Pharma, Janssen/J&J, Karuna, LB Pharma, Lundbeck, MedInCell, MedLink, Merck, Mindpax, Mitsubishi Tanabe Pharma, Maplight, Mylan, Neumora Therapeutics, Neuraxpharm, Neurocrine, Neurelis, Newron, Noven, Novo Nordisk, Otsuka, PPD Biotech, Recordati, Relmada, Response Pharmaeutical, Reviva, Rovi, Saladax, Sanofi, Seqirus, Servier, Sumitomo Pharma America, Sunovion, Sun Pharma, Supernus, Tabuk, Takeda, Teva, Terran, Tolmar, Vertex, Viatris and Xenon.

A bill on euthanasia and assisted suicide is currently under discussion in France. It proposes that only competent adults suffering from a serious and incurable condition that threatens their life in the medium term, or who are in an advanced or terminal phase, and experiencing unbearable physical or psychological suffering—either refractory to treatment or considered unbearable in the absence of treatment—may request medical assistance in dying (MAiD). However, French psychiatrists have expressed concerns about the bill, as it does not mandate a psychiatric evaluation, despite the high prevalence of mental disorders, including depression, in the general population. These disorders are even more frequent in end-of-life conditions and can significantly impact decision-making capacity and the wish to die. Depression, a common comorbidity in cancer—the leading cause of MAiD requests—affects approximately 15% of cancer patients but is often underdiagnosed and undertreated. The bill also raises concerns regarding its implications for suicide prevention. Some MAiD requests may stem from treatable psychiatric conditions rather than a well-considered end-of-life choice. Furthermore, a proposed obstruction offense could potentially criminalize suicide prevention efforts, complicating the role of mental health professionals. Uniquely, the French bill allows a third party chosen by the patient to administer the lethal substance, a provision not found in any other country. This raises significant ethical and psychological concerns regarding the emotional burden on the designated individual, who may experience distress, guilt, or long-term psychological repercussions from actively participating in assisted dying. Finally, the possibility of future expansion to include psychiatric-only indications, as seen in other countries, remains a critical issue requiring careful ethical and medical scrutiny.

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The Flemish Association of Psychiatry developed due care guidelines for medical assisted dying in cases of severe and unbearable psychiatric suffering, which were adopted by the Order of Physicians. In principle, there are therefore more than enough well-known recommendations on how to handle a request for termination of life from a patient with psychiatric issues.

Nevertheless, occasionally something goes wrong due to misinterpretation of the legal criteria or due to careless actions by the consulting or performing physician.

In 2010, a female patient died by euthanasia because of unbearable mental suffering, which was unacceptable for her family. The family decided to initiate a court case to have the inaccuracies in the decision-making process and the execution of the euthanasia evaluated by a judge. In 2020, three involved doctors, including a psychiatrist, were prosecuted for this euthanasia. An analysis of the court case and the media coverage of this case will be discussed.

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Medical assistance in dying is an increasingly available option for people suffering (solely) from psychiatric disorders. Initially promoted to alleviate the suffering of terminally ill people, a growing number of jurisdictions are adopting it for any cause of intractable and severe suffering, including mental disorders. Today, the BENELUX countries, along with Spain and Switzerland, explicitly authorise it or allow it de facto. Other countries, such as Canada, are considering implementing it. Although in jurisdictions where it is permitted it is argued that it is discriminatory not to consider mental suffering as sufficient cause, there are reasons for concern. The procedure is likely to be used as an alternative to care, that is, as a gentler form of suicide, more commonly used by women. The long-term impact of this practice must be considered, as it sends the message that mental illnesses may not be curable, and that it is not worth the effort to treat them, or to demand the necessary care. Furthermore, all of these factors must be considered in the context of highly stigmatized disorders to which clearly scarce resources are allocated.

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