In today’s column, I examine the recently announced national pledge by the U.S. Department of Health and Human Services (HHS) on advancing behavioral health quality and best practices in our nation’s mental health care. The pledge was signed by various national healthcare leaders, mental health experts, medical associations, behavioral care providers, and others who sought to signify their explicit support for the precepts embodied in the pledge.
My focus is on adding AI to that pledge. You see, the pledge doesn’t mention anything at all about AI, but in my view, the role of AI in advancing behavioral health quality and best practices is essential and inevitable. As such, I provide an augmented version of the principles embodied in the pledge, doing so to directly illuminate how AI is going to be a central element in improving mental health care. There is no doubt that AI has a huge impact when it comes to mental health care, and we must acknowledge and seek to prudently manage the role of AI for the betterment of societal and individual mental health.
Let’s talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage of the latest in AI, including identifying and explaining key AI complexities (see the link here).
AI And Mental Well-Being
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 an extensive listing of my well over one hundred analyses and postings, see the link here and the link here.
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 on an episode of CBS’s 60 Minutes; see the link here.
AI is being used widely for mental health purposes by individuals on an ad hoc basis, typically via everyday use of generative AI and large language models (LLMs). In addition, AI is being used by mental health professionals as a psychotherapeutic tool with their clients. I refer to this as an evolving transition from the classic dyad of therapist-client to becoming a new triad of therapist-AI-client; see my in-depth discussion at the link here. AI is a dual-use proposition, meaning that AI can be of great benefit to mental health and can also be lamentably detrimental to mental health. Crucial trade-offs must be considered and suitably balanced.
AI Providing Mental Health Guidance
Millions upon millions of people are using generative AI as their ad hoc mental health advisor (note that ChatGPT alone has over 1 billion weekly active users, a notable proportion of whom dip into mental health aspects; see my analysis at the link here). The overarching top-ranked use of contemporary generative AI and LLMs is to consult with the AI on mental health facets; see my coverage at the link here.
This popular usage makes abundant sense. You can access most of the major LLMs for nearly free or at a super low cost, doing so anywhere and at any time. Thus, if you have any mental health qualms that you want to chat about, all you need to do is log in to AI and proceed forthwith on a 24/7 basis.
There are significant worries that AI can readily go off the rails or otherwise dispense unsuitable or even egregiously inappropriate mental health advice. Banner headlines continue to announce lawsuits filed against AI makers such as OpenAI for their alleged lack of robust AI safeguards when it comes to AI-generated cognitive advisement.
Today’s generic LLMs, such as ChatGPT, GPT-5, Claude, Gemini, Grok, Copilot, and others (all known as general-purpose AI or GPAI), are not at all akin to the robust capabilities of human therapists. Meanwhile, specialized LLMs are being built to attain similar qualities (known as purpose-built AI or PBAI), but they are still primarily in the development and testing stages. See my extensive assessment at the link here.
Various State Laws On AI Mental Health
A beehive of activity is underway to craft new AI laws to rein in the AI-powered mental health advisement bonanza. See my comprehensive overview of state-level AI mental health laws at the link here. This is a matter weighing heavily on the public’s mind and currently is positioned in the hands of state legislators. Some believe that AI and AI makers are being allowed to run amok. New AI laws are vitally needed to protect society from this onslaught of ubiquitous AI that purportedly offers mental health guidance.
I have been meticulously reviewing the state-level AI laws that pertain to mental health, including my review of AI laws passed by specific states such as Illinois see the link here, Nevada see the link here, Utah see the link here, California see the link here, Vermont see the link here, Maine see the link here, and many other U.S. states newly passed AI laws. Those laws are scoped to prevail within their respective state boundaries. In that sense, these laws are applicable to AI usage within the particular state and do not directly bear on other states.
Congress has repeatedly waded into establishing an overarching federal law that would encompass AI. So far, no dice. The efforts have ultimately faded from view. Thus, at this time, there isn’t an overarching federal law devoted to these controversial AI matters. The big question will be to what degree a sweeping federal law would impact the numerous state-level AI laws. The odds are that many of the state-level laws would run afoul of a federal mandate, and a tsunami of legal cases would arise as a tussle between federal law and state law is undertaken. It surely will be a legal mess.
Readers might recall that I proposed a 7-step AI-law-making process that I believe could substantively help regulators to devise new AI laws that are on target and balanced; see my depiction at the link here. This has the added benefit of reducing what I refer to as AI-law legal debt. This refers to AI laws that, though they look pristine, contain hidden legal debt that must ultimately be paid. Legal glitches and law-based hitches are embedded into these new laws. My prediction is that AI makers will legally fight these AI laws on a tooth-and-nail basis, potentially successfully prevailing due to these laws being hastily written and passed without sufficient double-checking.
The New Pledge Released By HHS
On July 29, 2026, the U.S. Department of Health and Human Services released a pledge statement and met with various mental health industry notables to get signed support for the pledge. HHS Secretary Robert F. Kennedy, Jr., met with the various signees and emphasized that the goal of the pledge is to raise the standard of care throughout the nation and ensure that every patient can heal and thrive.
The pledge consists of these six principles:
- (1) Timely access to high-quality mental health and addiction treatment.
- (2) Evidence-based assessment, diagnosis, treatment, referral, and recovery support.
- (3) Measurement of quality, outcomes, accountability, and continuous improvement.
- (4) Patient-centered, recovery-focused care that supports long-term wellness.
- (5) Clinical expertise and individualized treatment decisions based on patient needs and the best available evidence.
- (6) Whole-person care is delivered, including addressing other chronic diseases.
AI for mental health dovetails into each one of those six principles. Let’s go ahead and briefly explore each principle and see how AI can be essential to attaining that principle.
#1: Timely Access To High-Quality Mental Health Care
Timely access to mental health guidance is perhaps the most often touted basis for why AI ought to have a significant role in this realm. AI can provide 24/7 engagement, screening, triage, navigation, psychoeducation, and overall mental health support. In addition, AI can help identify people who need urgent or specialized care and connect them with appropriate human services. AI can also extend scarce clinician capacity through documentation, care coordination, and between-visit support.
The main debate about timely access and AI is primarily concerned with whether AI can provide high-quality mental health care. Some insist that AI is only capable of low-intensity support and should not attempt to lean beyond its skis. Until AI is in the high-quality arena, sufficient control should be devised to keep humans from falling into a mental trap of assuming that AI is on par with human-provided therapy.
#2: Evidence-Based Mental Health Care
I’ve been repeatedly showcasing how AI can be instrumental to advancing evidence-based mental health care; see the link here and the link here, for example. AI can help clinicians synthesize patient histories, screening instruments, clinical notes, and other information; identify potentially relevant diagnostic considerations; suggest evidence-based interventions; flag contraindications or missing information; and recommend appropriate referrals. It can also continuously provide evidence-based behavioral interventions and recovery support, while keeping the clinician responsible for diagnosis and treatment decisions.
Researchers and practitioners who avoid using AI are missing out on the immense leverage that AI can provide when seeking to achieve heightened evidence-based precepts. In that same breath, please realize that AI should not become the thinker and instead should be the doer. An overreliance on AI for evidence-based mental health care is equally unwelcome.
#3: Measurement Of Mental Health Care
There is an old saying that you cannot properly manage something that you aren’t suitably measuring. That adage can readily apply to the management of our national mental health care efforts. There needs to be practical and sensible measurements of quality, outcomes, accountability, and a quantified means of striving for continuous improvement.
AI can make mental health care much more measurable. It can automatically collect and analyze patient-reported outcomes, symptom trajectories, engagement, treatment adherence, functional outcomes, and adverse events. AI can identify patterns across large populations, detect deterioration earlier, evaluate which interventions are working for whom, and generate feedback for clinicians and health systems. I’ve also noted that we could use the major LLMs as a potential source for gauging national mental health status and trends, namely by tapping into the vast database of user interactions, though this raises possible Big Brother concerns; see my assessment at the link here.
#4: Patient-Centered Mental Health Care
There are rather contentious viewpoints about AI and the topic of being patient-centered. Some believe that only human therapists can be patient-centered. This is presumably a human-to-human consideration. No AI can be as patient-centered. At first glance, that might seem logical. The thing is that human therapists are not perfect, and they are often overwhelmed with the volume of clients they provide care to. The therapists might aim to be patient-centered, but distractions and paying attention to specific details can be sacrificed because of an excessive workload.
AI can provide highly personalized, continuous support. It can retrieve an individual’s goals, preferences, treatment history, coping strategies, and progress; adapt educational and behavioral support accordingly; and help people monitor their own recovery. This potentially changes mental health care from episodic encounters to an ongoing relationship centered on the person’s goals and wellness.
#5: Individualized Treatment In Mental Health Care
Tapping again into the general fact that human therapists tend to be overwhelmed due to volume and that this makes things difficult when it comes to treating clients on a personalized basis, the same consideration applies to interest in individualized treatment. Sure, a therapist creates an individual treatment plan, but this is at times a template that is not as deeply individualized as might be preferred. AI can produce, maintain, and upkeep individualized treatment plans in significant ways; see my analysis at the link here.
AI can bring together all sorts of disparate data and readily assimilate that into individualized treatment. AI is highly scalable. Thus, this can be done for thousands, hundreds of thousands, and millions of people. AI can rapidly synthesize research on psychology and mental health, clinical guidelines, patient history, prior treatment responses, and relevant risk factors to then devise individualized options. AI can work hand-in-hand with therapists. Rather than replacing expertise, AI can potentially amplify the expertise of a clinician, particularly when clinicians are confronted with complex or uncommon cases.
#6: Whole-Person Oriented Mental Health Care
An ongoing challenge of mental health care is the aim to provide whole-person oriented care. A person might seek mental health care, but their physical health is not given due consideration as a synergistic or holistic perspective on the care that they need.
AI can help break down the fragmentation between mental and physical health. It can integrate information about mental health, chronic disease, medications, substance use, sleep, social determinants, lifestyle, and other factors to identify interactions and unmet needs. AI can also coordinate information among primary-care physicians, psychiatrists, therapists, specialists, and other providers, helping produce a more comprehensive picture of the individual.
The World We Are In
Those are the mainstay upbeat aspects of why AI needs to be included as an integral element of the pledge and its six principles. I mainly covered the upsides. We must also be on alert for the potential downsides. If AI is poorly utilized or allowed to be used in undermining ways, there is a strong chance of AI usurping the efforts to attain those six principles. In that sense, AI can be a tremendous booster but can also be an undesirable detractor that will sink the six principles.
A final thought for now. William Shakespeare famously made this remark: “It is not in the stars to hold our destiny but in ourselves.” The question of whether AI is going to be beneficial to mental health or be harmful to mental health is up to us as a society to determine. Let’s not allow random fate to decide. Destiny on this weighty aspect is in human hands.