Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response

Abstract

Legalized use of cannabis products and the rising interest in their therapeutic benefits have opened up new opportunities for therapy and marketing. However, the marked variability in formulations, administration modes, therapeutic regimens, and inter- and intra-subject responses make the standardization of medical cannabis-based regimens difficult. Legalization has made the cannabis market highly competitive and lowered the revenue margins. This study reviews some of the challenges in medical cannabis use and difficulties in standardizing its therapeutic regimens that hinder maximizing its beneficial effects. The development of tolerance toward cannabis and low adherence to chronic administration further impair its long-term beneficial effects. Digital medical cannabis is a cannabis product controlled by a second-generation artificial intelligence (AI) system that improves patient responses by increasing adherence and dealing with tolerance. Second-generation AI systems focus on a single patient's outcome and deal with the inter- and intra-subject variability in responses. The use of digital medical cannabis is expected to improve product standardization, maximize therapeutic benefits, reduce health care costs, and increase the revenue of companies. Digital medical cannabis offers several market differentiators for cannabis companies. This study presents a model for promoting the use of digital medical cannabis and presents its advantages for patients, clinicians, health care authorities, insurance companies, and cannabis manufacturers. Ongoing trials and real-world data on the use of these systems further support the use of digital medical cannabis for improved global health.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

“…Implementing variability into therapeutic regimens of anti-aging drugs, such as metformin, statins, anti-oxidants, and all types of medications and natural products, which showed some efficacy in pre-clinical and clinical studies, is expected to improve the response to these drugs. In parallel, using the digital pill with anti-depressants, anti-diabetics, cardiac drugs, chronic analgesics, anti-cancer drugs, and other medications which are commonly used in the aged population is expected to overcome the compensatory mechanisms and increase the response rate and the clinical benefits from these medications, improving quality of life and increasing lifespan ( Ilan, 2019e ; Ilan, 2019f ; Kenig and Ilan, 2019 ; Khoury and Ilan, 2019 ; Gelman et al, 2020 ; Ilan and Spigelman, 2020 ; Kessler et al, 2020 ; Potruch et al, 2020 ; Ilan, 2021a ; Ishay et al, 2021a ; Azmanov et al, 2021 ; Ishay et al, 2021b ; Hurvitz et al, 2021 ; Isahy and Ilan, 2021 ; Kenig et al, 2021 ; Khoury and Ilan, 2021 ; Kolben et al, 2021 ; Azmanov et al, 2022 ; Gelman et al, 2022 ).…”

“…The compensatory mechanisms that lead to a lack of a sustainable response evolve from mechanisms that differ between subjects and interventions ( Ilan, 2020c ). The compensatory responses lead to partial or complete loss of response in up to half of the users of numerous agents, including anti-inflammatory agents, anti-cancer drugs; anti-depressants, anti-epileptics analgesics, medical cannabis-based products, metabolic agents, and others ( Ilan, 2019e ; Ilan, 2019f ; Kenig and Ilan, 2019 ; Khoury and Ilan, 2019 ; Ilan, 2020c ; Gelman et al, 2020 ; Ilan and Spigelman, 2020 ; Kessler et al, 2020 ; Potruch et al, 2020 ; Ilan, 2021a ; Ishay et al, 2021a ; Azmanov et al, 2021 ; Ishay et al, 2021b ; Hurvitz et al, 2021 ; Isahy and Ilan, 2021 ; Kenig et al, 2021 ; Khoury and Ilan, 2021 ; Kolben et al, 2021 ; Azmanov et al, 2022 ; Gelman et al, 2022 ). Compensatory mechanisms also underlie the plateaus effects in training and other non-pharmacological interventions, hindering the ability to achieve a long-term effect ( Gelman et al, 2022 ).…”

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections

“…A digital system was developed for implementing variability and chronobiology-based regimen for overcoming drug resistance and improving the effectiveness of chronic medications [ 30 , 31 , 65 , 68 , 69 , 85–100 ]. Using variability to improve organ function was proposed based on studies showing its fundamental role in complex biological systems [ 64 , 70–73 , 99–105 ]. This second-generation digital system overcomes some of the major obstacles associated with the lack of penetration of first-generation systems in everyday clinical practice [ 74 , 106 , 107 ].…”

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

“…It is essential to establish and quantify cellular and whole organ patterns of variability and then implement them into the algorithm for improving accuracy [ 25 , 43 , 68 , 76 , 85 , 97 , 100 , 101 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 153 , 154 , 155 , 156 , 157 , 158 ]. Important information is also obtained by evaluating single-cell variabilities in gene expression, proteomics, metabolomics, and epitope expression performed on cells harvested from patients before and after therapy for chronic disease.…”