Artificial intelligence in psychiatry research, diagnosis, and therapy

Psychiatric disorders from EEG signals through deep learning models

Psychiatric disorders present diagnostic challenges due to individuals concealing their genuine emotions, and traditional methods relying on neurophysiological signals have limitations. Our study proposes an improved EEG-based diagnostic model employing Deep Learning (DL) techniques to address this. By experimenting with DL models on EEG data, we aimed to enhance psychiatric disorder diagnosis, offering promising implications for medical advancements. We utilized a dataset of 945 individuals, including 850 patients and 95 healthy subjects, focusing on six main and nine specific disorders. Quantitative EEG data were analyzed during resting states, featuring power spectral density (PSD) and functional connectivity (FC) across various frequency bands. Employing artificial neural networks (ANN), K nearest neighbors (KNN), Long short-term memory (LSTM), bidirectional Long short-term memory (Bi LSTM), and a hybrid CNN-LSTM model, we performed binary classification. Remarkably, all proposed models outperformed previous approaches, with the ANN achieving 96.83 % accuracy for obsessive-compulsive disorder using entire band features. CNN-LSTM attained the same accuracy for adjustment disorder, while KNN and LSTM achieved 98.94 % accuracy for acute stress disorder using specific feature sets. Notably, KNN and Bi-LSTM models reached 97.88 % accuracy for predicting obsessive-compulsive disorder. These findings underscore the potential of EEG as a cost-effective and accessible diagnostic tool for psychiatric disorders, complementing traditional methods like MRI. Our study's advanced DL models show promise in enhancing psychiatric disorder detection and monitoring, with significant implications for clinical application, inspiring hope for improved patient care and outcomes. The potential of EEG as a diagnostic tool for psychiatric disorders is substantial, as it can lead to improved patient care and outcomes in the field of psychiatry.

Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition.

Exploring biologically oriented precision mental health initiatives for the care of patients with eating disorders: A narrative review

OBJECTIVE

Eating disorders (EDs) represent a major public health burden. Increasingly, studies suggest mental health (MH) fields are failing to improve the effectiveness of treatments and that alternative models of care must be considered. Precision mental health (PMH) seeks to tailor treatment to individual needs and relies on a comprehensive understanding of the neurobiological and physiological underpinnings of mental illness.

METHODS

In this narrative review, published literature with focus on biological application of PMH strategies for EDs is reviewed and summarised.

RESULTS

A total of 39 articles were retained for the review covering a variety of themes with relevance to PMH. Many studies of biological markers with PMH applicability focused on anorexia nervosa. Although a variety of potential PMH research applications were identified, the review failed to identify any evidence of implementation into routine ED practice.

CONCLUSIONS

Despite the theoretical merit of biological application of PMH in ED treatment, clinical applications for standard practice are lacking. There is a need to invest further in studies that seek to identify biological markers and investigate neurobiological underpinnings of disease in hopes of targeting and developing treatments that can be better tailored to the individualised needs of patients.

Diagnostic accuracy of large language models in psychiatry

INTRODUCTION

Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5.

METHODS

We curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text.

RESULTS

The AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models.

DISCUSSION

The application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application.

CONCLUSION

AI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry.

Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran

Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.

Navigating merits and limits on the current perspectives and ethical challenges in the utilization of artificial intelligence in psychiatry - An exploratory mixed methods study

BACKGROUND

The integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an informed and ethically aware psychiatric workforce capable of integrating AI into practice responsibly.

METHODS

A mixed-methods study was conducted to assess the outcomes of the "CONNECT with AI" - (Collaborative Opportunity to Navigate and Negotiate Ethical Challenges and Trials with Artificial Intelligence) workshop, aimed at exploring AI's ethical implications and applications in psychiatry. This workshop featured presentations, discussions, and scenario analyses focusing on AI's role in mental health care. Pre- and post-workshop questionnaires and focus group discussions evaluated participants' perspectives, and ethical understanding regarding AI in psychiatry.

RESULTS

Participants exhibited a cautious optimism towards AI, recognizing its potential to augment mental health care while expressing concerns over ethical usage, patient-doctor relationships, and AI's practical application in patient care. The workshop significantly improved participants' ethical understanding, highlighting a substantial knowledge gap and the need for further education in AI among psychiatrists.

CONCLUSION

The study underscores the necessity of continuous education and ethical guideline development for psychiatrists in the era of AI, emphasizing collaborative efforts in AI system design to ensure they meet clinical needs ethically and effectively. Future initiatives should aim to broaden psychiatrists' exposure to AI, fostering a deeper understanding and integration of AI technologies in psychiatric practice.

The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy

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This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

Developing a machine learning-based short form of the positive and negative syndrome scale

BACKGROUND AND HYPOTHESIS

The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores.

STUDY DESIGN

The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa.

STUDY RESULTS

Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6-24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items.

CONCLUSION

The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

Mental disturbance impacting wellness dimensions: Resources and open research directions

In light of the unparalleled pressure faced by the healthcare system, there arises a pressing need for innovative solutions to comprehensively assess the overall well-being of individuals affected by mental health issues. With the objective of advancing AI-driven mental health analysis towards fine-grained analysis, we develop and publicly release our datasets, MULTIWD and WELLXPLAIN, specifically designed to capture the impact of mental disturbances on wellness dimensions in self-narrated texts. To this end, we make two major contributions. First, our examination focuses on the identification of one or more of the six distinct wellness dimensions evident within a given text, shedding light on the significant ramifications of mental disturbance, which, in turn, can perpetuate further mental unrest. Second, we conducting an extensive analysis of the textual cues that signify the presence of various wellness dimensions. We delve into the content of the text, examining specific linguistic and contextual markers that provide indications of the wellness dimensions being discussed. Finally, we open up future research directions to facilitate advancements in the domain of AI-driven approaches for fine-grained mental health analysis. This framework aims to establish and validate new clinical categories for mental distress, bridging the gap between mental wellness and illness, in response to the higher prevalence of distress compared to illnesses.

Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study's results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

Appraising the performance of ChatGPT in psychiatry using 100 clinical case vignettes

BACKGROUND

ChatGPT has emerged as the most advanced and rapidly developing large language chatbot system. With its immense potential ranging from answering a simple query to cracking highly competitive medical exams, ChatGPT continues to impress the scientists and researchers worldwide giving room for more discussions regarding its utility in various fields. One such field of attention is Psychiatry. With suboptimal diagnosis and treatment, assuring mental health and well-being is a challenge in many countries, particularly developing nations. To this regard, we conducted an evaluation to assess the performance of ChatGPT 3.5 in Psychiatry using clinical cases to provide evidence-based information regarding the implication of ChatGPT 3.5 in enhancing mental health and well-being.

METHODS

ChatGPT 3.5 was used in this experimental study to initiate the conversations and collect responses to clinical vignettes in Psychiatry. Using 100 clinical case vignettes, the replies were assessed by expert faculties from the Department of Psychiatry. There were 100 different psychiatric illnesses represented in the cases. We recorded and assessed the initial ChatGPT 3.5 responses. The evaluation was conducted using the objective of questions that were put forth at the conclusion of the case, and the aim of the questions was divided into 10 categories. The grading was completed by taking the mean value of the scores provided by the evaluators. Graphs and tables were used to represent the grades.

RESULTS

The evaluation report suggests that ChatGPT 3.5 fared extremely well in Psychiatry by receiving "Grade A" ratings in 61 out of 100 cases, "Grade B" ratings in 31, and "Grade C" ratings in 8. Majority of the queries were concerned with the management strategies, which were followed by diagnosis, differential diagnosis, assessment, investigation, counselling, clinical reasoning, ethical reasoning, prognosis, and request acceptance. ChatGPT 3.5 performed extremely well, especially in generating management strategies followed by diagnoses for different psychiatric conditions. There were no responses which were graded "D" indicating that there were no errors in the diagnosis or response for clinical care. Only a few discrepancies and additional details were missed in a few responses that received a "Grade C" CONCLUSION: It is evident from our study that ChatGPT 3.5 has appreciable knowledge and interpretation skills in Psychiatry. Thus, ChatGPT 3.5 undoubtedly has the potential to transform the field of Medicine and we emphasize its utility in Psychiatry through the finding of our study. However, for any AI model to be successful, assuring the reliability, validation of information, proper guidelines and implementation framework are necessary.

The future of psychiatry with artificial intelligence: can the man-machine duo redefine the tenets?

As one of the largest contributors of morbidity and mortality, psychiatric disorders are anticipated to triple in prevalence over the coming decade or so. Major obstacles to psychiatric care include stigma, funding constraints, and a dearth of resources and psychiatrists. The main thrust of our present-day discussion has been towards the direction of how machine learning and artificial intelligence could influence the way that patients experience care. To better grasp the issues regarding trust, privacy, and autonomy, their societal and ethical ramifications need to be probed. There is always the possibility that the artificial mind could malfunction or exhibit behavioral abnormalities. An in-depth philosophical understanding of these possibilities in both human and artificial intelligence could offer correlational insights into the robotic management of mental disorders in the future. This article looks into the role of artificial intelligence, the different challenges associated with it, as well as the perspectives in the management of such mental illnesses as depression, anxiety, and schizophrenia.