NetraMark Holdings Inc. (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: 8TV) and the National U.S. based National Institute for Mental Health (NIMH) have teamed up to analyze clinical trials on the use of ketamine to treat various psychiatric disorders.
Announced on Monday, the collaboration has NIMH using NetraMark’s artificial intelligence and advanced machine learning tech to perform evaluate NIMH data from clinical trials in a variety of disorders.
The overall aim is to determine and identify variables characterizing ketamine response, placebo response and note any adverse events that researchers could use to help guide patient selection and design for future clinical trials.
“The trials included in this collaborative research project comprise small datasets that are challenging to interpret because they do not reflect the totality of the disorder they represent,” Dr. Joseph Geraci, PhD, Founder, and Chief Scientific Officer of NetraMark, said. “NetraAI is designed and tuned to work with these smaller datasets by discerning variables that define explainable subpopulations.”
Geraci says that this method improves predictive model accuracy. They anticipate that employing NetraAI in this research plan will offer a comprehensive understanding of the effects and mechanisms of action (MOA) of ketamine in various psychiatric disorders, ultimately aiming to inform future clinical trials for a therapy that could meet critical unmet needs.
This collaboration advances the integration of AI into psychiatric research, aiming to enhance precision medicine for these complex, heterogeneous disorders.
Unlike other AI-based methods, NetraAI is uniquely engineered to include focus mechanisms that separate small datasets into explainable and unexplainable subsets. This separation helps identify collections of patients that could otherwise lead to suboptimal overfit models and inaccurate insights due to poor correlations with the variables involved.
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NetraAI uses subsets to influence treatment
NetraAI uses the explainable subsets to derive insights and hypotheses, including factors that influence treatment and placebo responses, as well as adverse events. This approach significantly increases the chances of clinical trial success. In contrast, other AI methods lack these focus mechanisms and assign every patient to a class, leading to overfitting that drowns out critical information which could have improved a trial’s success rate.
In recent months, artificial intelligence has seen an explosive rise, transforming various sectors with rapid advancements.
Generative AI has significantly impacted the creative industries, with companies like Runway producing high-quality AI-generated videos that are attracting major film studios. Companies like Runway are using this technology for everything from creating special effects to generating deepfake avatars for marketing.
The surge in AI development has also been fuelled by substantial investments. In 2023, private investment in generative AI skyrocketed to USD$25.2 billion, marking a dramatic increase from previous years, according to the World Economic Forum. This influx of funding is helping to push the boundaries of what AI can achieve, from advancing scientific research to improving productivity in various industries.
As AI continues to evolve, its applications are becoming more widespread and sophisticated, reshaping our interactions with technology and prompting ongoing discussions about its ethical implications and societal impact, especially with all the companies presently chasing artificial general intelligence (AGI).
AGI is largely considered to be the next evolutionary stage in artificial intelligence. Vancouver-based Verses AI (CBOE: VERS) (OTCQB: VRSSF) says that artificial intelligence will progress through three stages.
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The three stages of artificial intelligence development
Verses calls the first stage Narrow AI. It represents the current state-of-the-art in artificial intelligence.
These systems perform specific tasks or solve specific problems within a limited domain and do not exhibit the kind of general intelligence found in humans. Examples of Narrow AI include speech and image recognition software, natural language processing software, most current generative AI.
AGI systems possess the flexible, general-purpose intelligence found in humans. This enables it to adapt to new situations, learn and understand, and perform a wide range of tasks and activities.
Verses calls the third phase of AI progress Artificial Super Intelligence (ASI). This sophisticated AI would not only operate generally across domains but also surpass human abilities, even those of experts, in virtually all respects.
Whereas the present state of AI reduces these complex systems to the function of calculators, the arrival of AGI would signal an entirely different shift.
For example, the potential impact of AGI on clinical trial analysis could be transformative.
AGI can process vast amounts of complex clinical data more efficiently and accurately than current AI systems, leading to more precise identification of patterns and correlations that might be missed by human analysts or less advanced AI models.
It can also enhance patient stratification by analyzing multiple data points—genomic, phenotypic, environmental—to create highly accurate patient subgroups, thus tailoring treatments and improving trial outcomes by ensuring that the right patients receive the appropriate interventions.
Additionally, AGI can simulate numerous trial designs to predict which would yield the best outcomes, including determining optimal sample sizes, intervention types, and endpoints, potentially reducing the time and cost associated with clinical trials.
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