AI in Psychiatry: Changing the Landscape of Mental Health Care

Image by Nicole Slocum / MJH Life Sciences using AI

Computer systems that perform tasks that usually require human intelligence are known as artificial intelligence (AI). These tasks may include problem-solving, language or speech understanding, learning from experience, and even decision-making.

Large language models (LLMs) are a type of AI specifically designed for understanding and generating natural language. These models are pretrained on large amounts of diverse text data, which allow them to perform language-related tasks including translation, summarization, question answering, and text completion. Given a prompt or context, the LLM can generate fluent, coherent, human-like language responses.

The generative pretrained transformer (GPT) is an LLM developed by OpenAI. There have also been rapid enhancements in the models since OpenAI released GPT-1 in 2018, leading to better complex reasoning and cohesion. The model’s learning parameters grew from 110 million (GPT-1 in 2018) to 175 billion (GPT-3 in 2020).^1,2

Launched on March 14, 2023, GPT-4 is the latest model in the GPT series from OpenAI that is thought to have been trained on more than 1 trillion parameters. It boasts more advanced reasoning and greater ability to handle more complex instructions, and it is 40% more likely to produce factual responses than GPT-3.5.³ However, GPT is limited in that it can continue to return biased, inappropriate, and inaccurate responses.

Revolutionary Potential of AI and Generative Language Models

A 2023 report from McKinsey & Company estimates that generative AI’s impact on productivity could add the equivalent of $2.6 trillion to $4.⁴ trillion annually.⁴ LLMs have the potential to transform a variety of industries. From automating and personalizing e-commerce customer support tasks to quickly analyzing and summarizing large volumes of legal text, the applications for enhancing productivity are extraordinary (Table 1).⁵

Table 1. Potential Applications for Generative Language Models Across Industries and Areas⁵

Applications in Psychiatry

Like many industries and medical specialties, the application of AI and LLMs in psychiatry could transform the ways patients and providers receive and deliver care, respectively. Despite training on general data, ChatGPT performs many medical and health care tasks well.⁶

Mental health clinicians may look to AI- and LLM-powered tools to help reduce administrative burden and provide clinical decision support. Patients may benefit from these tools for education, self-care, communication with their care teams, and more.

The industry is optimistic in its applications, as findings from studies have shown high accuracy for potential applications in clinical decision support. Deep learning models have shown high accuracy in predicting mental health disorder and severity.⁷ ChatGPT achieved final diagnosis accuracy of 76.9% (95% CI, 67.8%-86.1%) in findings from a study of 36 clinical vignettes.⁸

However, it is important to note that this study was carried out in an experimental environment with a small case sample. These vignettes may not approximate a patient’s real-world clinical presentation or the text input a user would naturally enter into the chatbot interface.

Several organizations are piloting the application of ChatGPT, and outcomes of real-world deployment in clinical settings are missing.^6,9 However, real-world clinical use of AI has not been without controversy. A little-known mental health app went viral when it replaced human responses with AI without telling patients.¹⁰

The capabilities of the technology are evolving rapidly, and use and ethics must play catch-up. Although accuracy increases and nonsensical, confabulated outputs are reduced with each subsequent model enhancement, major limitations and concerns with the tool persist.¹¹

Limitations of Generative Language

Despite the enthusiasm for the potential impact AI can make in psychiatry, the industry is cautious and slow to implement the technology given its limitations. Although there are many potential limitations, common concerns include accuracy, quality, and transparency of training data; confabulated outputs (hallucinations); and biases.

Diagnosis accuracy can be high in findings from vignette studies; however, these rates dip when case complexity rises. Findings from one clinical vignette study showed that ChatGPT-4 achieved 100% diagnosis accuracy within the top 3 suggested diagnoses for common cases, whereas human medical doctors solved 90% within the top 2 suggestions but did not reach 100% with up to 10 suggestions.

For rare cases, only 40% are solved within the first diagnosis suggestion and it takes 8 or more suggestions to achieve 90% diagnosis accuracy. By contrast, medical doctors approached 50% accuracy within the top 2 suggestions.¹²

Transparency, quality, and contemporary training data remain a challenge. ChatGPT was trained on a large corpus of text data, such as webpages and books.² If the GPT model does not handle high- and low-quality evidence differently, this can lead to incorrect or inaccurate outputs.¹³ With billions of parameters, model retraining requires intensive, expensive computation.¹¹

ChatGPT-3.5 (the free version currently available to the public) uses training data up to September 2021, and it cannot access or leverage real-time data and events from after this date.¹⁴

LLMs can generate confabulated outputs known as hallucinations. Hallucinations are particularly concerning in medical settings, where incorrect information may have implications for morbidity and mortality. ChatGPT may make a correct choice but give nonsensical explanations. In findings from one study, ChatGPT constructed false references within a research paper.¹⁵

In another study, when asked to answer a clinical question about the feasibility, safety, and effectiveness of an implantable cardioverter-defibrillator, ChatGPT referred to 2 different studies that do not exist.¹⁶ ChatGPT may also hallucinate medical knowledge or mathematical deductions.¹¹

Biases from the training data—such as those regarding gender, race, and culture—make ChatGPT capable of generating stereotyped and prejudiced content.^2,17 In its initial release, when asked to identify who would be a good scientist based on race and gender, ChatGPT said a “White male” and responded negatively for all other subgroups.¹⁸

Combating Limitations

In November 2023, OpenAI introduced custom GPTs as a powerful new feature of their flagship product, ChatGPT. The custom GPT feature allows individual users to tailor the LLM to their specific parameters using plain language prompts. This new feature allows users to input data sets and resources while also telling the custom GPT which references should be used in responses.

In medicine, AI has demonstrated promise in its ability to correctly answer medical questions, but what currently limits wider adoption in clinical decision-making is the risk of hallucinations and inaccurate answers.

LLMs do not differentiate between the validity of their sources but rather aggregate the best response using word patterns. In medicine, a response generated by a peer-reviewed journal reference is considered higher quality and more reliable and very different from one generated by sources in an internet chat room. The custom GPT builder works to address this issue.

Methods

Neuro Scholar, a custom GPT on the OpenAI ChatGPT platform, was created with a focus on neuroscience and psychiatry. Neuro Scholar was instructed to communicate in a formal tone, reflecting an academic and professional nature suitable for discussing complex, evidence-based topics in neuroscience and psychiatry. It is designed to ask clarifying questions when needed to ensure accurate and relevant responses.

Neuro Scholar focuses on providing comprehensive, evidence-based information on neuroscience and psychiatry topics, strictly adhering to scientific literature that was uploaded to the reference collection. The model was also instructed to notify the user if the answer was not available in the uploaded resources and to warn the user if generalized knowledge (references outside the uploaded data set) was used.

The Neuro Scholar reference collection includes textbooks and other resources that encompass a wide range of topics in neuroscience, psychiatry, and related fields. The uploaded resources used to build this custom GPT are listed in Table 2.

Table 2. Neuro Scholar Custom GPT Inputs and Training Resources

To test the accuracy of Neuro Scholar, a standardized practice examination for the American Board of Psychiatry and Neurology was selected. Practice examination 1 of Psychiatry Test Preparation and Review Manual, Third Edition consisted of 150 questions. The practice examination was administered to Neuro Scholar and ChatGPT-3.5.

Results

ChatGPT-3.5 correctly answered 125 of 150 questions, whereas Neuro Scholar correctly answered 145 of 150 questions, achieving 96.67% accuracy on the practice exam (Figure).

Figure. ChatGPT-3.5 vs Neuro Scholar Custom GPT Performance in the ABPN Practice Examination

The 5 questions that Neuro Scholar answered incorrectly are listed in Table 3; ChatGPT-3.5 also missed these questions.

Table 3. Questions Answered Incorrectly by Neuro Scholar Custom GPT

Discussion

In just 1 year, AI and LLMs have rapidly evolved and are reaching a tipping point with regard to educational and clinical viability. The previously mentioned proof-of-concept experiment demonstrates that customized generative AI can improve accuracy and reduce hallucinations through control of which resources the model uses. In medicine, AI hallucinations can have disastrous consequences. Efforts to improve AI accuracy must also include efforts to eliminate inaccurate responses.

This proof-of-concept experiment also highlights concerns regarding intellectual property ownership within AI models. The Neuro Scholar custom GPT was built using multiple psychiatry textbooks that were acquired through the secondary market. Recently, OpenAI announced a partnership with publisher Axel Springer^.19

This partnership will formalize OpenAI’s use of the publisher’s content as resources for generative responses. As AI continues to evolve, further guidance will be needed regarding the use of copyrighted materials such as journal articles, textbooks, and reference guides.

Plain language programming of custom AI models carries many exciting implications in the world of psychiatry. In medical education, students can study by using AI prompts with responses that are focused on the specific learning concept.

As a tool for clinical support, custom AI models can be configured to provide algorithms that reference all available treatment guidelines for a respective condition. Lastly, industry within psychiatry can harness these models to provide medical affairs information while controlling the specific resources referenced.

Concluding Thoughts

AI was the transformative business development of 2023, and it will continue to evolve and change the landscape of medicine and psychiatry. As clinicians, it is imperative for us to understand the potential of AI in psychiatry but also to understand and remain wary of its limitations. AI may help psychiatry address chronic problems including adherence to treatment guidelines, access shortages, and didactic education across multiple professions and training settings.

Mr Asbach is a psychiatric physician associate and serves as associate director of interventional psychiatry at DENT Neurologic Institute in New York. Ms Menon is an independent researcher who is currently freelancing in advisory and contract roles developing clinical AI products. Mr Long is a physician assistant student at Daemen University in Amherst, New York.

References

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