Stanford Initiative Leverages AI To Robustly Transform Mental Health Research And Therapy

In today’s column, I explore the latest efforts to transform mental health research and therapy into being less subjective and more objective in its ongoing pursuits.

This kind of transformation is especially spurred via the use of advanced AI, including leveraging deep learning (DL), machine learning (ML), artificial neural networks (ANN), generative AI, and large language models (LLMs). It is a vital pursuit well worth undertaking. Expectations are strong that great results and new insights will be gleaned accordingly.

Let’s talk about it.

This analysis of AI breakthroughs is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).

AI And Mental Health Therapy

As a quick background, I’ve been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For a quick summary of some of my posted columns on this evolving topic, see the link here, which briefly recaps about forty of the over one hundred column postings that I’ve made on the subject.

There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors too. I frequently speak up about these pressing matters, including in an appearance last year on an episode of CBS 60 Minutes, see the link here.

If you are new to the topic of AI for mental health, you might want to consider reading my recent analysis that also recounts a highly innovative initiative at the Stanford University Department of Psychiatry and Behavioral Sciences called AI4MH, see the link here. Indeed, today’s discussion is substantively shaped around a recent seminar conducted by AI4MH.

The Subjective Vs. The Objective

Let’s begin with a cursory unpacking of what is generally thought of as a type of rivalry or balance amid being subjective versus objective.

The conceptualization of “objective” consists of a quality or property intended to convey that there are hard facts, proven principles and precepts, clear-cut observations, reproducible results, and other highly tangible systemic elements at play. In contrast, “subjective” is characterized as largely speculative, sentiment-based, open to interpretation, and otherwise less ironclad.

Where does the consideration of subjective vs. objective often arise?

You might be surprised to know that the question of subjective versus objective has a longstanding root in two fields of endeavor, namely psychology and physics. Yes, it turns out that psychology and physics have historically been domains that richly illuminate the dialogue regarding subjective versus objective. The general sense is that people perceive physics as tilted more toward the objective and less toward the subjective, while the perception of psychology is that it is a field angled toward the subjective side more so than the objective.

Turn back the clock to the 1890s, in which the famed Danish professor Harald Hoffding made these notable points about psychology and physics (source: “Outlines of Psychology” by Harald Hoffding, London Macmillan, 1891):

• “Psychology is the science of mind — that is the shortest description we can give of the subject of the present inquiries.”
• “It serves merely to mark psychology as the science of that which thinks, feels, and wills, in contrast with physics as the science of that which moves in space and occupies space.”
• “These two provinces include everything that can be the subject of human research.”

You might notice the rather stunning point that psychology and physics are themselves inclusive of everything that could potentially be the subject of human research. That’s amazingly alluring to those in the psychology and physics fields, while perhaps not quite as affable for all other domains.

In any case, on the thorny matter of subjective versus objective in the psychology realm, we can recall Pavlov’s remarks made in 1930:

• “I am convinced that an important stage of human thought will have been reached when the physiological and the psychological, the objective and the subjective, are actually united, when the tormenting conflicts or contradictions between my consciousness and my body will have been factually resolved or discarded.” (source: “Physiology of the Higher Nervous Activity” by Ivan Petrovich Pavlov, 1930, in C. Murchison (Ed.), Psychologies of 1930, Clark University Press).

Pavlov’s comments reflect a longstanding aspiration of the field of psychology to ascertain and verify bona fide means to augment the subjective aspects of mental health analysis with more exacting objective measures and precepts.

The final word on this goes to Albert Einstein as to the heady matter:

• “Body and soul are not two different things, but only two different ways of perceiving the same thing. Similarly, physics and psychology are only different attempts to link our experiences together by way of systematic thought.” (source: Albert Einstein in 1937 as cited in “Albert Einstein: The Human Side” by Helen Dukas and Banesh Hoffman, Princeton University Press, 1979).

It’s always an uphill battle to refute remarks made by Einstein, so let’s take them as they are.

Recent Seminar By Stanford AI4MH

Shifting gears, the topic of psychology and the challenging properties of subjective vs. objective was a major theme during a recent seminar undertaken by Stanford University on May 28, 2025, at the Stanford campus.

Conducted by the initiative known as AI4MH (Artificial Intelligence for Mental Health), see the link here, within the Stanford School of Medicine, Department of Psychiatry and Behavioral Sciences, the session was entitled “Insights from AI4MH Faculty: Transforming Mental Health Research with AI” and a video recording of the session can be found at the link here.

The moderator and the three speakers consisted of:

• Moderator: Dr. Kilian M Pohl, AI4MH Lead, Professor of Psychiatry and Behavioral Sciences (Major Labs and Incubator) and, by courtesy, of Electrical Engineering.
• First speaker: Dr. Kaustubh Supekar, Clinical Associate Professor, Psychiatry and Behavioral Sciences, Stanford University School of Medicine.
• Second speaker: Dr. Ehsan Adeli, Assistant Professor (Research) of Psychiatry and Behavioral Sciences (Public Mental Health and Population Sciences) and, by courtesy, of Computer Science and of Biomedical Data Science, Stanford University.
• Third speaker: Dr. Shannon Wiltsey Stirman, Professor of Psychiatry and Behavioral Sciences (Public Mental Health and Population Sciences), Stanford University.

I attended the session and will provide a recap and analysis here. In addition, I opted to look at various research papers by the speakers. I encompass selected aspects from the papers to further whet your appetite for learning more about the weighty insights provided during the seminar and based on their respective in-depth research studies.

I’ll proceed next in the same sequence as occurred during the seminar, covering each speaker one at a time, and then offer some concluding thoughts.

The Brain-Mind Matters

The human brain consists of around 86 billion neurons and approximately 100 trillion synapses. This elaborate organ in our noggin is often referred to in the AI field as the said-to-be wetware of humans. That’s a cheeky sendoff of computer-based hardware and software.

Somehow, in ways that we still aren’t quite sure, the human brain or wetware gives rise to our minds and our ability to think. In turn, we are guided in what we do and how we act via the miracle of what’s happening in our minds. For my related discussion about the Theory of Mind (ToM) and its relationship to the AI realm, see the link here.

In the presentation by Dr. Kaustubh Supekar, he keenly pointed out that the brain-mind indubitably is the source of our mental health and ought to be closely studied when trying to ascertain the causes of mental disorders. He and his team are using AI to derive brain fingerprints that can be associated with mental disorders.

It’s quite exciting to envision that we could eventually end up with a tight mapping between the inner workings of the brain-mind and how mental disorders manifest within the brain-mind. Imagine the incredible possibilities of anticipating, remedying, or at least aiding those incurring mental disorders.

In case you aren’t familiar with the formal definition of what mental disorders consist of, I covered the DSM-5 guidelines in a posting on AI-driven therapy using DSM-5, see the link here, and included this definition from the well-known manual:

• “A mental disorder is a syndrome characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning.”

DSM-5 is a widely accepted standard and is an acronym for the Diagnostic and Statistical Manual of Mental Disorders fifth edition, which is promulgated by the American Psychiatric Association (APA). The DSM-5 guidebook or manual serves as a venerated professional reference for practicing mental health professionals.

In a recent research article that Dr. Kaustubh Supekar was the lead author of, entitled “Robust And Replicable Functional Brain Signatures Of 22q11.2 Deletion Syndrome And Associated Psychosis: A Deep Neural Network-Based Multi-Cohort Study” by Kaustubh Supekar, Carlo de los Angeles, Sikanth Ryali, Leila Kushan, Charlie Schleifer, Gabriela Repetto, Nicolas Crossley, Tony Simon, Carrie Bearden, and Vinod Meno, Molecular Psychiatry, April 2024, these salient points were made (excerpts):

• “Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date.”
• “A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis.”
• “Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls.”

The study aimed to find relationships between those having a particular chromosomal omission, known as DiGeorge syndrome or technically as 22q11.2 deletion syndrome (DS), and linking the brain patterns of those individuals to common psychosis symptoms. The brain-related data was examined via the use of an AI-based artificial neural network (a specialized version involving space-time or spatiotemporal analyses underlying the data, referred to as stDNN).

This and other such studies are significant steps in the erstwhile direction of mapping brain-mind formulations to the nature of mental disorders.

Exhibited Behavior Greatly Matters

Faithful readers might recall my prediction that ambient intelligence (AmI) would be a rapidly expanding field and will dramatically inevitably change the nature of our lives, see the link here.

What is ambient intelligence?

Simply stated, it is a mishmash term depicting the use of AI to bring together data from electronic devices and do so with a focus on detecting and reacting to human presence. This catchphrase got its start in the 1990s when it was considered state-of-the-art to have mobile devices and the Internet of Things (IoT) was gaining prominence. It is a crucial aspect of ubiquitous computing.

Ambient intelligence has made strong strides due to advances in AI and advances in ubiquitous technologies. Costs are getting lower and lower. Embedded devices are here and there, along with the devices seemingly invisible to those within their scope. The AI enables adaptability and personalization.

In the second presentation of the AI4MH seminar, Dr. Ehsan Adeli notably pointed out that we can make use of exhibited behaviors to try and aid the detection and mitigation of mental health issues.

But how can we capture exhibited behavior?

One strident answer is to lean into ambient intelligence.

In a research article that he served as a co-author, entitled “Ethical Issues In Using Ambient Intelligence In Healthcare Settings” by Nicole Martinez-Martin, Zelun Luo, Amit Kaushal, Ehsan Adeli, Albert Haque, Sara S Kelly, Sarah Wieten, Mildred K Cho, David Magnus, Li Fei-Fei, Kevin Schulman, and Arnold Milstein, Lancet Digital Health, December 2020, these salient points were made (excerpts):

• “Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (e.g., imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data.”
• “Ambient sensors are placed in hospital settings (e.g., intensive care units [ICUs] and operating rooms, to monitor the activities of clinicians, staff, and patients, as well as in daily living spaces like independent living or community care settings, to gather data relevant to managing care for older people, chronic disease management, or mental health problems.”
• “As with other artificial intelligence technologies introduced into health care, ambient intelligence is expected to have an effect on the clinical relationship. The use of ambient intelligence systems takes the practice of medicine further from the traditional dyadic physician-patient relationship.”

The idea is that by observing the exhibited behavior of a person, we can potentially link this to their mental health status.

Furthermore, via the appropriate use of AI, the AI might be able to detect when someone is having mental health difficulties or perhaps incurring an actual mental health disorder. The AI could in turn notify clinicians or others, including the person themselves, as suitably determined.

In a sense, this opens the door to undertaking continuous assessment of neuropsychiatric symptoms (NPS). Of course, as directly noted by Dr. Ehsan Adeli, the enabling of AmI for this purpose brings with it the importance of considering aspects of privacy and other AI ethics and patient ethics caveats underlying when to best use these growing capabilities.

Evidence-Based Approaches Do Matter

Being evidence-based is a hot topic, aptly so.

The trend toward evidence-based medicine and healthcare has been ongoing and aims to improve both research and practice, doing so in a classic less subjective, and more objective systematic way. The American Psychological Association (APA) defines evidence-based practice in psychology (EBPP) as “the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences.”

The third speaker in the AI4MH seminar was Dr. Shannon Wiltsey Stirman, a top researcher with a focus on how to facilitate the high-quality delivery of evidence-based psychosocial (EBPs) interventions. Among her research work is a framework for identifying and classifying adaptations made to EBPs in routine care.

On the matter of frameworks, Dr. Stirman’s presentation included a discussion about a newly formulated framework associated with evaluating AI-based mental health apps. The innovative and well-needed framework had been devised with several of her fellow researchers. In a co-authored paper entitled “Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework” by Elizabeth Stade, Johannes Eichstaedt, Jane Kim, and Shannon Wiltsey Stirman, Technology, Mind, and Behavior, March 2025, these salient points were made (excerpts):

• “To ensure the responsible deployment of AI-mental health applications, a principled method for evaluating and reporting on AI-mental health applications is needed.”
• “To address this need, we introduce the Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI) framework for mental health applications.”
• “The READI framework comprises considerations of Safety, Privacy/Confidentiality, Equity, Effectiveness, Engagement, and Implementation. The READI framework outlines key criteria for assessing the readiness of AI-mental health applications for clinical deployment, offering a structured approach for evaluating these technologies and reporting findings.”

Longtime readers know that I have been calling for an assessment framework like this for quite a while.

For example, when OpenAI first allowed ChatGPT users to craft customized GPTs, there was a sudden surge in GPT-based applets that purportedly performed mental health therapy via the use of ChatGPT. In my review of those GPTs, I pointed out that many were not only vacuous, but they were at times dangerous in the sense that the advice being dispensed by these wantonly shaped ChatGPT applets was erroneous and misguided (see my extensive coverage at the link here and the link here).

I have also repeatedly applauded the FTC for going after those who tout false claims about their AI for mental health apps (see my indication at the link here). Just about anyone can readily stand up a generative AI app that they claim is suitable for mental health therapy. They might have zero experience, zero training, and otherwise be completely absent from any credentials associated with a mental health professional.

Meanwhile, consumers are at a loss to know which mental health apps are prudent and useful and which ones are problematic and ought to be avoided. It is for this reason that I have sought a kind of Consumer Reports scoring that might be used to differentiate AI mental health apps (see my discussion at the link here).

The new READI framework is a substantial step in that profoundly needed direction.

Momentous Human Payoff Ahead

Moving the needle on the subjective vs. objective preponderance in psychology is going to take earnest and undeterred energy and attention.

Newbie researchers especially are encouraged to pursue these novel efforts. Seasoned researchers might consider adjusting their usual methods to also incorporate AI, when suitable. The use of AI can be a handy tool and demonstrative aid. I’ve delineated the many ways that AI has already inspired and assisted psychology, and likewise, how psychology has aided and inspired advances in AI, see the link here for that discussion.

There is a great deal at stake in terms of transforming psychology and the behavioral sciences as far forward as we can aim to achieve. Besides bolstering mental health, which certainly is crucial and laudable, Charles Darwin made an even grander point in his “On the Origin of Species by Means of Natural Selection” in 1859:

• “In the distant future, I see open fields for far more important researches. Psychology will be based on a new foundation, that of the necessary acquirement of each mental power and capacity by gradation. Light will be thrown on the origin of man and his history.

You see, the stakes also include revealing the origins of humankind and our storied history.

Boom, drop the mic.

Some might say it is ironic that AI as a computing machine would potentially have a hand in the discovery of that origin, but it isn’t that far-fetched since AI is in fact made by the hand of humanity. It’s our self-devised tool in an expanding toolkit to understand the world.

And which gladly includes two very favored domains, e.g., the close and dear cousins of psychology and physics.