BFRBs vs. OCD: Similarities and Differences

This blog was originally posted by the TLC Foundation for BFRBs

Body-focused repetitive behaviors (BFRBs) and obsessive-compulsive disorder (OCD) are two distinct mental health conditions that share some similarities but also have significant differences. BFRBs involve repetitive, self-grooming behaviors that can cause physical damage, such as hair pulling or skin picking. On the other hand, OCD is a condition characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions) performed to alleviate anxiety. 

While both conditions involve repetitive behaviors and can impact daily life, their underlying mechanisms, triggers, and treatment approaches differ. This article explores the key similarities and differences between BFRBs and OCD to better understand these complex conditions.

Similarities Between BFRBs and OCD

Most professionals view BFRBs and OCD as similar conditions due to the similarity in symptoms, such as compulsivity and repetitive behaviors. These two conditions share several similar systems and are usually a reaction to triggering factors such as stress and anxiety. Below are some of their similarities.

Repetitive Behaviors

Individuals dealing with BFRBs often engage in various repetitive behaviors such as hair pulling, lip biting, or skin picking. These actions are usually challenging to control and are frequently triggered by stress or anxiety. One may indulge in the habit subconsciously to find instant relief from the trigger. 

Individuals with OCD often experience intrusive thoughts that result in repetitive behaviors known as compulsions. Some common compulsions include washing hands and repetitively checking or counting to alleviate the stress caused by obsessive thoughts. In both conditions, the repetitive behaviors are often exacerbated by stress and anxiety, and individuals may adapt these behaviors as a coping mechanism.

Impulse Control

Closely related to repetitive behaviors is the concept of impulse control. Both BFRBs and OCD involve challenges in this area, albeit in different ways. Individuals with BFRBs and OCD may find it hard to control the urge to perform repetitive behaviors. This is because these repetitive behaviors often relieve tension. Despite knowing the consequences of these behaviors, the desire to indulge in them is usually irresistible. 

For example, individuals with BFRBs understand that hair pulling may affect their appearance, but they cannot refrain from doing it. OCD occurs as a result of intrusive thoughts whereby one believes that if they do not perform a specific action, the stressor won’t go away. These intrusive thoughts often cause anxiety, which can be eased by engaging in the said repetitive behavior.

Onset and Course

Having examined the behavioral aspects, let’s now consider how these conditions develop over time. The onset of these two conditions shares several similarities regarding age, triggers, and psychological mechanisms. 

The onset of both conditions is usually during childhood or adolescence and often coincides with various developmental changes and stressors. For individuals with BFRBs, the repetitive behaviors alleviate stress and anxiety instantly. At the same time, for those with OCD, performing the compulsions temporarily relieves them from the stress caused by their intrusive thoughts. The cognitive patterns involve repetitive actions, intrusive thoughts, and a lack of impulse control. In BFRBs, the urge to engage in these repetitive behaviors can be intrusive and persistent, while in OCD, one’s obsessions create a sense of urgency, which leads to the adoption of compulsive actions.

Neurobiological Factors

To fully understand the similarities between BFRBs and OCD, we must delve deeper into their biological underpinnings. Both conditions have a genetic origin and are associated with neurobiological factors. Neurobiological studies indicate that the impulse control and emotional regulation difficulties for people with BFRBs and OCD are often caused by abnormalities in brain regions that are responsible for impulse control and habit formation. Therefore, the underlying brain mechanism may result in the onset and development of both conditions. It is not uncommon for individuals to have both BFRBs and OCD or for both conditions to coincide with other mental health conditions, usually depression and anxiety. The overlap is generally because they typically share common underlying factors that play a part in their severity and development.

Differences Between OCD and BFRBs

While BFRBs and OCD share several commonalities, it’s equally important to understand their distinct characteristics, from the symptoms to the underlying mechanisms. Let’s explore the key differences that distinguish these two conditions.

Nature of the Behavior

First and foremost, let’s examine how the behaviors associated with each condition differ in their fundamental nature. Individuals dealing with these two conditions adopt diverse behaviors as coping mechanisms for their triggers. In BFRBs, the behaviors adopted, such as trichotillomania (hair-pulling) or cheek-biting, usually result in physical harm. However, regardless of the consequences, one always feels relieved when picking their skin or pulling their hair. 

OCD, on the other hand, involves a wide range of compulsions, from washing to organizing, checking, and counting. Compulsive behaviors are performed due to intrusive thoughts that make one think that if they fail to indulge in a specific behavior, they might get hurt, or there might be other negative consequences.

Presence of Obsessions

Another crucial distinction lies in the cognitive processes behind these behaviors. Generally, BFRBs do not involve obsessive thoughts. The primary focus on BFRBs is usually more on the physical behavior and not the fear of specific consequences. 

However, the major characteristic of OCD is intrusive thoughts, which increase the urge to indulge in particular behaviors for relief. The thoughts are usually persistent with unwanted images that result in distress. 

People with BFRBs DO NOT report that if they do not pick on their skin, something terrible will happen. Instead, they report that picking or pulling their hair helps relieve them from intense and negative emotions. These behaviors, therefore, serve a self-regulatory function, unlike in OCD, where the repetitive behavior calms them from their intrusive thoughts.

Triggers

The nature of triggers for each condition is closely related to the presence or absence of obsessions. The primary trigger in BFRBs is stress and anxiety, but for OCD, the main trigger is intrusive thoughts, which then result in anxiety. OCD and BFRBs triggers differ in several ways, often resulting in different outcomes. OCD triggers often result in one taking measures to prevent harm, while for BFRBs, one uses the adopted behaviors to regulate and manage intense emotions. The nature of thoughts is an essential distinguishing factor, seeing as OCD involves intrusive and obsessive thoughts that trigger specific behaviors adopted to prevent harm. The purpose of compulsions in OCD is to reduce the anxiety caused by the obsessive thoughts, while in BFRBs, the behaviors are for emotional relief.

Awareness

Beyond triggers, the level of conscious awareness also differentiates these two conditions. Those dealing with BFRBs usually find themselves biting their nails or even pulling their hair subconsciously. Individuals with OCD are generally aware of their intrusive thoughts and are compelled to adopt specific behaviors as a response to these thoughts. Individuals with OCD are often aware of their compulsions and understand when they are being irrational, but they are unable to control themselves. Compared to people with OCD, those with BFRBs often find their behaviors more rewarding than distressing.

Treatment

Finally, while both conditions may benefit from cognitive behavioral therapy, the specific approach to treatment varies significantly. For individuals with BFRBs, the focus is on behavior modification and awareness, achieved through habit reversal training. For OCD, the emphasis is often placed on exposure to anxiety-provoking thoughts to help an individual tolerate anxiety, which prevents compulsive behavior.

Bottom Line 

While BFRBs and OCD can coexist, they are distinct disorders with unique manifestations despite sharing some similarities. The key distinctions between these conditions are evident in their underlying mechanisms and treatment approaches.

Both involve compulsive behaviors, but their purposes differ. BFRBs primarily serve as subconscious tools for emotional regulation. OCD compulsions are conscious attempts to alleviate anxiety and prevent perceived harmful consequences. BFRB behaviors often occur with limited conscious awareness, while OCD sufferers are typically more aware of their compulsive actions.

Both conditions can significantly affect daily functioning and social interactions.BFRBs may lead to physical injuries and lowered self-esteem due to visible effects. OCD can cause severe anxiety and time-consuming rituals that interfere with daily activities.BFRB treatment emphasizes behavior modification and awareness techniques, while OCD treatment often involves exposure therapy to reduce anxiety responses.

Understanding these distinctions is crucial for accurate diagnosis and effective treatment. While both conditions present challenges, with proper support and intervention, individuals with BFRBs or OCD can learn to manage their symptoms and improve their overall quality of life.

The post BFRBs vs. OCD: Similarities and Differences appeared first on International OCD Foundation.

Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas

ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.

AI Agents Are Coming: 5-Stage Taxonomy of Language-Based AI Systems for Psychiatry, Psychotherapy, and Counseling

The rapid evolution of large language models has accelerated the development of agentic artificial intelligence (AI) systems capable of pursuing autonomous goals, creating an urgent need for structural frameworks in psychiatry and psychotherapy. While existing classifications often draw parallels to autonomous driving, this paper argues that the mental health domain requires a distinct, domain-specific theoretical foundation, as the 2 domains differ fundamentally in their semantic, ideographic, and epistemological demands. Furthermore, they differ in their end goals, for which we introduce terms such as agentic guidance capability. To guide clinicians and researchers through these developments, we propose a 5-stage taxonomy for language-based AI systems that differentiates technical functionality from clinical effectiveness. The taxonomy progresses from level 1 (knowledge level), in which systems perform static benchmark tasks, to level 2 (elementary level), characterized by dynamic engagement in specific therapeutic microskills. At level 3 (integration level), systems achieve consistency across and within modules, as well as basic case-level conceptualization suitable for blended therapy under human oversight. Level 4 (saturation level) describes therapist-in-the-loop systems capable of autonomous functioning with minimal supervision, whereas level 5 (mastery level) represents AI systems that are technically capable of performing autonomous therapy. By distinguishing technical functionality from clinical effectiveness, we conclude that level 4 or level 5 performance does not automatically translate into full treatment effectiveness, even if high treatment fidelity can be achieved. We conclude by emphasizing the need to shift benchmarking from static knowledge tests to dynamic evaluations of therapeutic capabilities in order to safely navigate the transition toward autonomous care.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/24bd74c372b92b7db87be2834a55944b" />

The Download: Claude’s inner workings and OpenAI’s “super app”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Anthropic found a hidden space where Claude puzzles over concepts

The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. 

Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside its flagship LLM, Claude.

The J-space contains words related to the response a model is working on but may not ultimately produce. If Claude were a person (which it is not), you might say these hidden words reveal what’s on its mind before it actually speaks. 

Read the full story on what they found.

—Will Douglas Heaven

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has unveiled its long-awaited “super app” 
ChatGPT Work blends its chatbot, coding tool, and new models. (Reuters $)
+ It’s designed to do your work for you and with you. (Ars Technica)
+ And arrived the same day as OpenAI’s GPT 5.6 models. (NYT $)
+ It’s also developing a fully automated researcher. (MIT Technology Review)

2  Humanoids have performed teleoperated surgery on living animals
In the world-first, they removed gallbladders from pigs. (Ars Technica)
+ The human work behind humanoids is hidden. (MIT Technology Review)
 
3 SK Hynix has landed the largest US listing by a foreign company
The South Korean chip giant raised $26.5 billion. (CNN)
+ Demand for AI data centres has led its profits to skyrocket. (Guardian)
+ But its jumbo share sale may be a sign of overheated times. (FT $)
+ South Korea’s hottest bachelors are chip workers. (MIT Technology Review)
 
4 Tencent is leading a deal to unwind Meta’s $2 billion Manus acquisition
It’s in talks to become the Chinese AI startup’s largest shareholder. (FT $)
+ Tencent will reportedly buy Manus for no less ​than $2 billion. (Reuters $)
+ Beijing had ordered Meta to unwind the acquisition. (Bloomberg $)
 
5 Resuscitated human retinas responded to light 10 hours after death
It’s a big step towards eye transplants that restore vision. (New Scientist $)
+ As is a new device that revives dead eyeballs. (MIT Technology Review)
 
6 Meta has started charging for AI access
A new version of Muse Spark has a paid tier for developers. (Quartz) 
+ Meta also plans to start producing an AI chip in September. (Reuters $)
 
7 OpenAI and Google have sold AI models to blacklisted China groups
Via Singapore-based subsidiaries of Alibaba, Baidu and Tencent. (FT $)

8 A daughter tested an AI “death bot” of her father
The technology provided both comfort and unease. (New Yorker $)

9 An astronomer says the hunt for alien life needs more statistics
He wants to replace speculation with mathematical frameworks. (Quanta)

10 Pokémon Go players turned Times Square into a giant battlefield
More than 1,500 fans finally fulfilled the game’s 2016 launch promise. (Wired $)
+ Pokémon Go is also training world models. (MIT Technology Review)

Quote of the day

“When we’re talking about AI, we love the hype, we get excited about it. The damn thing never actually lands in practice.”

—Vijay Janapa Reddi, an engineering professor at Harvard University, tells Wired why he’s skeptical about grand plans for AI.

One More Thing

a hand putting a pigeon into hatch in a missile

B.F. SKINNER FOUNDATION


Why we should thank pigeons for our AI breakthroughs

In 1943, psychologist B.F. Skinner led a secret government project to make bombs more precise. His idea: teach pigeons to guide missiles by pecking at targets on a screen inside a warhead. To train them, Skinner rewarded the birds with food when they made the right decisions, using trial and error to shape their behavior.

Unsurprisingly, the military never deployed Skinner’s kamikaze pigeons. Yet his experiments convinced him that pigeons were “an extremely reliable instrument” for studying learning.  

Decades later, those same principles would help power reinforcement learning, the technology behind some of today’s most advanced AI systems.

Discover how pigeons inspired one of AI’s most powerful techniques.

—Ben Crair

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Here’s a splendid selection of this year’s NSW architecture award winners.
+ Photographers have captured the Strawberry Moon’s golden glow in stunning detail.
+ Idiocracy is the film that best exemplifies the “American experience,” according to a new poll. Look back at the prescient comedy with this Screen Junkies trailer.
+ Get ready for the weekend with this psychedelic house journey from Jamie xx b2b Caribou.

Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust.

(OTTAWA) July 8, 2026 — New polling shows approximately six million people in Canada used AI-enabled tools for mental health support in the past year and most find them effective. Today, the Mental Health Commission of Canada (the Commission), in partnership with Mental Health Research Canada (MHRC) and Pollara Strategic Insights, releases the first nationally representative data on how people in Canada engage with digitally supported mental health tools, including AI and virtual care, across every province and demographic.

Quick Facts:

  • 1 in 7 people in Canada used AI mental health tools in the past year
  • Three out of four who used AI and virtual mental health services found them effective for their well-being
  • Only 14 % trust AI tools, just 2% trust them completely
  • 40 % of AI users said they were more likely to seek professional care
  • Nearly half (45%) who accessed mental health care did so virtually, in whole or in part

WHY IT MATTERS

People in Canada are turning to AI as a convenient way to access mental health support.  AI-enabled tools may offer greater convenience and accessibility. Among those surveyed, AI is being used because it is:

  • Free or low-cost; 46% of AI users cite this as the reason they use it during a time when financial stress is itself a cause for anxiety.
  • Always available; 44% of AI users cite 24/7 access.
  • Immediate and convenient; it can be used from anywhere without travelling or waiting for an appointment. For someone in rural Canada, it saves time and travel costs.
  • Seemingly private; 39 % of AI service users cite private, anonymous support as a reason for use, while privacy and data protection remain key public concerns.

AI is most used for general well-being (42%), companionship (36%), and mild-to-moderate stress (36%), and 40% of AI users said they were more likely to seek professional care.

WHO IS USING IT AND HOW MUCH DO THEY TRUST IT?

Use is higher among people in Canada under 35 (27%; 29% among men aged 25–34), newcomers to Canada (28%), racialized people in Canada (23%), and 2SLGBTQI+ communities (20%), populations that may experience greater barriers to traditional care.

Overall, trust remains low, particularly for AI-enabled tools, where only 2% of people in Canada trust them completely. People in Canada over 55 show the lowest adoption and trust.

VIRTUAL CARE: EFFECTIVE AND MORE TRUSTED BUT FALLS SHORT OF IN-PERSON SERVICES

45% of people in Canada who used mental health services in the past year did so virtually, with 75% reporting positive outcomes. However, nearly 1 in 3 prefer a hybrid model that combines virtual and in-person services. The data signals what people in Canada need: well-designed tools for safer digital mental health care that they can trust.

THE COMMISSION OFFERS GUIDANCE FOR THE DIGITAL MENTAL HEALTH ERA

The Commission is Canada’s trusted resource for safe digital mental health — assessing apps and tools, setting evidence-based standards, and leading the national conversation on guidance for AI in mental health and substance use health care.

As virtual services and AI-enabled tools continue to expand rapidly across the mental health landscape, there is a growing need for evidence-based insight into how people in Canada engage with, understand, and perceive them. The Commission partnered with MHRC to leverage their ongoing national polling initiative and provide timely insights into usage, attitudes, and concerns related to e-mental health and AI.

The polling is clear: people in Canada want to close the gap between availability and trust. The Commission is working with the Canadian Centre on Substance Use and Addiction and collaborators, provincial governments, technology developers, and health system partners to establish guidance for AI.

“Six million people in Canada have already used AI for mental health support and most found it convenient and effective for their well-being. It is critical that AI is safe and equitable to increase public trust and reduce harms.” – Lili-Anna Pereša, President and Chief Executive Officer, Mental Health Commission of Canada

“The people turning to digitally-supported mental health tools are often those facing some of the greatest barriers to care. Making sure these tools are safe, effective, evidence-based and human-centred is a matter of equity. Ongoing research is essential to understanding where they help and where safeguards are needed.”– Akela Peoples, Chief Executive Officer, Mental Health Research Canada

About Mental Health Commission of Canada
As an independent, not-for-profit with charitable status, the Commission collaborates with leading experts and organizations nationally and internationally, including with people with lived and living experience, to develop national guidelines, standards and strategies, promote innovation and best practices, reduce stigma, increase mental health literacy, and support all levels of government to improve mental health outcomes for everyone living in Canada.  The Commission is Canada’s trusted resource for digital mental health best practices with the e-Mental Health Strategy for Canada, app assessment, e-modules for e-mental health implementation, and AI guidance for mental health and substance use health.

About Mental Health Research Canada
As an independent national charity, MHRC works hard to enable a future where mental health in Canada is transformed using evidence, data and stakeholder engagement. We unite researchers, communities, and people with lived experience to bridge gaps in care through national population polling, rapid data reporting, and partnerships that inform policy to improve outcomes. Learn more at www.mhrc.ca

About the Polling
Conducted by Pollara Strategic Insights in partnership with Mental Health Research Canada and the Mental Health Commission of Canada, this national poll (n=3,519) is the first representative data on AI use for mental health in Canada. Full findings: https://mentalhealthcommission.ca/AI-polling-report

About the Funding
The views in this report solely represent the views of the Mental Health Commission of Canada. Production of this report is made possible through financial contribution from Health Canada.

Media Contact
Heather Bakken, Pendulum Group
email: heather@pendulumgroup.ca 
cell: 613-406-5432

The post Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust. appeared first on Mental Health Commission of Canada.

$10 Million Donation Boosts Treatment Development for Ultra-Rare Disease

The Clayco Foundation has gifted $10 million to researchers at the Perelman School of Medicine at the University of Pennsylvania to help them to development a potential treatment for the ultra-rare disease retinal vasculopathy with cerebral leukoencephalopathy (RVCL).

“This is a disease that affects so many organs across the body, so a typical targeted gene therapy doesn’t work,” said Jonathan Miner, MD, PhD, an associate professor of Rheumatology at Penn, who leads the work. “We have developed something that labels abnormal proteins—just like you would a package. The body’s cells can then read that label and ship the protein to a specific location in the cell for destruction.”

RVCL is an inherited, autosomal dominant disease caused by mutations in a gene called TREX1 that affects around 200 people across the world. The mutation causes DNA damage and premature death of the endothelial cells that line small blood vessels. Over time, the surrounding tissue receives less blood and oxygen and begins to malfunction or die.

People with the condition usually present in mid‑adulthood with slow vision loss from retinal vasculopathy and then develop stroke‑like episodes, cognitive decline, and psychiatric symptoms linked to white‑matter damage in the brain. There is no current disease-modifying treatment for the condition and people with the condition usually die in mid-late adulthood.

Miner directs the RVCL Research Center at Penn. He and his team are working on several potential therapies for this very rare disease including the one funded by this donation, a small molecule drug candidate that can degrade damaged TREX1 proteins in the body.

“In mice with the human mutation who get this disease, the degrader molecule saves their lives,” explained Miner in a press statement. “It stops organ damage and stabilizes them from further harms.”

The candidate therapy works by linking the faulty protein to an enzyme, an E3 ligase, that marks unwanted proteins so the cell can break them down. The damaged protein is then rapidly cleared by the cell’s disposal machinery, while normal proteins are spared.

The $10 million donation from the Clayco Foundation will help Miner and colleagues move this drug candidate closer to the clinic.

The Clayco Foundation is Chicago‑based and is closely linked to the design‑build firm Clayco, which was founded in the 1980’s by Bob Clark. Clark’s wife Ellen died of RVCL in 2010 and because of this the foundation has a strong focus on funding research that helps people with the condition.

Miner and colleagues are also working on a couple of genetic therapies for RVCL using CRISPR and prime-editing technology. They are also assessing if crizanlizumab, a P‑selectin–blocking monoclonal antibody currently approved to reduce sickle cell crises, could be repurposed to also treat RVCL.

The post $10 Million Donation Boosts Treatment Development for Ultra-Rare Disease appeared first on Inside Precision Medicine.

The foundational elements of AI architecture that IT leaders need to scale

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future.

Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems.

Four elements of AI architecture you can count on

The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves.

1. Prepare data for AI at scale

Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.

Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems.

As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil.

An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value.

Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval.

2. Use context engineering to deliver the right data to every AI query

Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently.

Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model.

Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times.

“Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says.

3. Build AI governance and LLM observability in from the start

Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations.

In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges.

Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight.

Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient.

For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset.

When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points.

Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve.

In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps.

“Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says.

4. Keep humans in the loop

The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.”

As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management. 

Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in fresh thinking, it also presents high costs in system continuity, institutional understanding, and innovation. Human-centered strategy needs to be built into AI execution stages to ensure smooth implementation. 

As Adil says, “Many aspects of the stack are moving very, very fast, but institutional knowledge and the ability to adapt remain durable.

Thoughtful AI investment for future growth

As AI systems evolve from single-task assistants to increasingly autonomous agents, the organizations best positioned to benefit will be those that invest in the underlying systems, governance, and expertise that make AI reliable at scale.

Tech leaders who focus on these fundamentals can move effectively from experimentation to reliable, production-level deployment in the medium term, confident that these elements will remain relevant and adaptable amid constant advancements.

“We fundamentally believe that with these tools, velocity of work will get much faster,” Adil says. “We are really focused on how we can do work with these tools in ways we had not thought of before.”

Learn more about how Elastic is building an AI-first enterprise with these core foundational components.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

MH-POWER for College Students on the Spectrum

Conditions: College Students on the Autism Spectrum With Mental Health Challenges

Interventions: Behavioral: The MH-POWER program

Sponsors: University of Texas Rio Grande Valley; The American Occupational Therapy Foundation

Recruiting