Acta Psychiatrica Scandinavica最新文献

The paper in this issue by Fava and colleagues¹ highlights features of the Diagnostic Criteria for Psychosomatic Research (DCPR) and stresses how the DCPR was inspired by Engel's biopsychosocial model.²

Engel wrote that his ‘proposed biopsychosocial model provides a blueprint for research, a framework for teaching, and a design for action in the real world of healthcare’.² Since then, the biopsychosocial model has been widely adopted, particularly by clinicians and clinical teachers. However, it has also attracted criticism. Some have argued that it says nothing about the subjective experience of the patient.³ Others have gone further in their criticism, claiming that individualising treatment to each patient, as the model implies, gives rise to eclectic freedom which ‘borders on anarchy’ and merely replaces the dogmas which the model was intended to protect against (notably the reductionism of the biomedical model) with other dogmas.⁴ Responding to criticisms that Engel's model lacks a sound theoretical basis, Bolton⁵ noted that the original model needs to be understood as of its time and that despite its limitations, the model anticipated the crucial role in health and illness of ‘concepts such as regulation and dysregulation, information and communication and function and dysfunction’. It has been argued that the main problem with the model as Engel proposed it is that it is too general. One proposed solution to this is to elaborate specific, evidence-based, models for different diagnoses or conditions.⁶ The DCPR represents a different but effective solution to the same problem. Instead of elaborating the details of the biopsychosocial model separately for different conditions (a monumental undertaking), the DCPR aims to describe particular transdiagnostic states (termed ‘syndromes’) which can occur as features of the experience of illness.

Fava et al.¹ illustrate the DCPR by characterising some of its syndromes. These are all patterns of responses to life situations involving illness, reflecting dysregulation and/or dysfunction. They are termed syndromes to distinguish them from disorders or diseases which form the basis of standard diagnostic classifications.⁷ The syndromes were intended to be descriptive and without any pathogenic implications, although with progress in research and understanding, this assumption might now be challenged. The original syndromes were not intended to be exhaustive and indeed the original DCPR has been revised to include two additional syndromes.⁸ An important feature of the syndromes is that they each include (or overlay) biological, psychological and social components. Allostatic overload is a prime example, manifestly showing biological, psychological and so

In the context of artificial intelligence (AI), the term “hallucinations” is used to refer to false responses generated by conversational agents/chatbots or other generative AI tools (artificial intelligence/machine learning models capable of generating content such as text, speech, images and video).^{1, 2} This metaphor is unfortunate as it is both imprecise and, due to its clear negative connotation, stigmatizing for the many individuals experiencing hallucinations—those with schizophrenia and other psychotic disorders in particular.³ While the stigma associated with the misunderstood use of the term hallucinations in relation to generative AI is highly unfortunate, this editorial will propose that it is, however, possible that generative AI may also be helpful and reduce stigma for those experiencing hallucinations and/or delusions.

For individuals with schizophrenia and other psychotic disorders it can be difficult to communicate the nature and quality of their hallucinations and delusions to relatives, friends as well as to the healthcare professionals involved in their treatment—often due to the recipients not handling this communication well enough.⁴ This may lead to lack of understanding of the suffering associated with these symptoms and could, in turn, contribute to detachment from loved ones, stigma and suboptimal treatment.⁵ Therefore, tools to facilitate communication regarding hallucinations and delusions are sorely needed.

This editorial will propose that AI tools capable of generating images (e.g., DALL·E⁶) and video (e.g., Sora⁷) may be used to facilitate (highly affordable) illustration of- and, thereby, communication regarding hallucinations and delusions experienced by people with schizophrenia and other psychotic disorders. Interestingly, this approach has recently been described in an ophthalmological case report, where Woods and colleagues report on the diagnosing and treatment of a patient with monocular Charles Bonnet syndrome secondary to optic neuritis, where generative AI was used to successfully illustrate the patient's visual hallucination.⁸

Figure 1 shows three hypothetical examples of hallucinations and delusions illustrated using the version of DALL·E⁶ embedded within ChatGPT-4⁹ at the time of writing—along with the exact prompts that were used to generate the images. Two images of each set of symptoms are shown (read from left to right)—highlighting the importance of the wording of the prompts and the possibility to revise images if they are not accurately portraying the symptoms in question.

While this is, by no means, a formal assessment of the quality of the generated illustrations or their usefulness, it does seem that this approach has the potential to aid communication r