Sage Journals: Discover world-class research

Abstract

Introduction:

Adhering to treatment plans can be challenging for medical cannabis patients. According to the constrained-disorder principle (CDP), biological systems are defined by their degree of variability. CDP-based second-generation artificial intelligence (AI) systems use personalized variability signatures to improve chronic medication response.

Aim:

We retrospectively analyzed real-world data regarding chronic pain patients using the second generation of artificial intelligence systems to improve adherence to medical cannabis and increase its effectiveness.

Design and methods:

A retrospective analysis of real-world data of 27 patients using prescribed medical cannabis for chronic pain was conducted. Patients received treatment according to a regimen provided by the CDP-based second-generation AI Altus Care™ app that managed the product’s dosage and administration times. The app offers a therapeutic regimen by varying dosages and administration times within predefined ranges. We included 16 patients who participated for more than a week. We assessed adherence to therapy and clinical response in real life based on pain scale measurements.

Results:

The patients were followed up for 64 days (30–189). Second-generation, AI-based, personalized regimens had a high engagement rate and adherence. 50% of patients showed a high compliance rate. Chronic pain improved in patients who reported their pain score.

Summary:

This preliminary real-world data analysis suggests that an algorithm-based approach using a second-generation AI system may enhance the adherence to and clinical effectiveness of medical cannabis. These findings require confirmation through prospective controlled studies.

Keywords

Artificial intelligence cannabis digital health adherence

Relevance for public health

The increasing use of medical cannabis encounters various challenges, including the absence of standardized treatment regimens, low adherence rates, and significant variability in responses both between individuals and within the same individual. We conducted a retrospective analysis of real-world data concerning chronic pain patients to assess the effectiveness of second-generation artificial intelligence systems in improving adherence to medical cannabis usage. Our findings indicate that personalized regimens based on second-generation AI achieved a high engagement rate and adherence. Fifty percent of patients demonstrated strong compliance. Notably, chronic pain improved in patients who reported their pain scores. This real-world data suggests that an algorithm-based approach utilizing a second-generation AI system can enhance both adherence to medical cannabis and its clinical effectiveness.

Introduction

Various medical cannabis formulations are used for numerous indications, including chronic pain, inflammation, autism, sleep disturbances, and many more.^1–5 Chronic use of cannabis-based products is plagued by low adherence and decreased effectiveness.⁶ Formulations, therapeutic regimens, and inter and intra-subject variability in responses make it difficult to standardize therapy and maximize these products’ therapeutic potential.⁷ Cannabis is one of the most widely used recreational psychoactive substances in the world. In 2018, it was estimated that there were 192 million cannabis users, which represents 3.9% of the global population aged 15–64 years. Despite ongoing efforts to regulate cannabis worldwide, significant differences in country policies and usage patterns may unpredictably and disproportionately increase the prevalence of cannabis use disorders.⁸

Patients and doctors demonstrate low engagement with digital applications aimed at improving adherence. A recent review of studies that examined medical apps for enhancing patients’ responses showed that among 17 studies that demonstrated some level of effectiveness, eight showcased statistically significant improvements.⁹ Only a single study showed improved objective measures beyond relying on patient-reported questionnaires.⁹

Medication management apps improve patient education by allowing healthcare providers to educate patients on the importance of medication adherence and effective medication management.¹⁰

The Constrained Disorder Principle (CDP) describes biological systems based on their inherent variability.^11,12 All systems are characterized by a dynamic level of variability, which facilitates better adaptation to both internal and external disturbances. According to the CDP, malfunctions in systems occur due to either insufficient or excessive variability.^13–15

The partial or complete loss of response significantly contributes to low adherence to chronic medications.¹³ CDP-based second-generation artificial intelligence (AI) systems implement variability into therapeutic regimens within a predefined therapeutic range to improve the effectiveness of chronic medications and overcome the loss of response.^10,16,17

The term “digital medical cannabis” refers to any cannabis formulation whose administration is controlled by second-generation artificial intelligence systems.⁷ Patients with chronic diseases benefit from the system by improving clinically meaningful outcomes, such as pro-BNP levels, 6-min walk tests, and Kansas City cardiomyopathy scores in congestive heart failure.^7,16,18–39

The paper summarizes retrospective preliminary proof of concept real-world data of chronic pain patients receiving digital medical cannabis. In real-world settings, we examined the effect of an algorithm-based regimen on cannabis adherence and effectiveness. To assess compliance, we quantified the frequency of patient logins to the app to monitor their treatment plan. Effectiveness was evaluated through the pain rating recorded within the app.

Design and methods

Retrospective analysis of real-world data: Data from patients receiving medical cannabis for chronic pain was analyzed retrospectively and non-interventionally. The study analyzed data of individuals who received cannabis from Hadassah Ein Kerem Hospital in Jerusalem throughout 2022. Their physicians offered subjects to use the app for their cannabis usage.

Data collection: Patients’ data is anonymously collected in the cloud and analyzed retrospectively. Several self-entered outcome measures were followed. The pain scale ranged from one to ten, where a lower score indicated no pain while a higher score indicated the most intense pain. The percentage of app logins to the total planned logins was measured; a patient receiving treatment three times a day was calculated as the number of logins divided by three times the number of follow-up days. Improvement in pain was defined as any decrease in the pain scale throughout the follow-up period. Each patient served as their baseline, allowing for a comparison of their progress from the beginning of the trial. Moreover, good compliance was defined as logging into the app for more than 40% of the planned logins based on previous studies showing low adherence to these regimens.⁴⁰

Second-generation AI system: Altus Care™ is a cellular phone-based product of Area9 Innovation Apps, part of Area9 Group, that allows easy digitization of treatment plans or research protocols and remote implementation of these plans. The Altus Care™ platform has been combined with treatment algorithms that use second-generation AI to provide random alterations in the dosing and times of administration of medications within a physician’s predefined range and serves as a reminder for patients to take their medications (Oberon Sciences, Israel). Israel’s Ministry of Health has approved its use.

Treatment regimens provided by the app: Physicians prepared a predefined range of minimal and maximal daily dosages and timing frames for each patient. In the app, physicians provide a therapeutic regimen, including the cannabis formulation, the dose range, and the timing of administration. The daily dose and frequency were set to not exceed the patients’ dose before using the app. The Altus Care™ app was downloaded on the subjects’ cellular phones. Reminders are sent to patients to take the designated formulation within the predetermined range. Patients can use the app to answer a daily pain scale questionnaire that their physician can view.

Results

Subjects

The universal sampling data of 27 patients who used medical cannabis according to a regimen provided by the app was analyzed. The analysis did not include patients who used the product for less than a week.

The demographics of the patients in the study are presented in Table 1. Sixteen patients who used the app for more than a week were analyzed, including six males and 10 females, with a mean age of 47. The diagnosis for which subjects received the cannabis included four fibromyalgia, three Back pain, two neuropathy, two Crohn’s disease, three joint pain due to different autoimmune diseases, one chronic pain syndrome after physical trauma, and one chronic abdominal pain without a diagnosis. Data were available for a median follow-up of 64 days (range 30–189). In half of the patients, cannabis oil drops were taken, while in the other half, cannabis was smoked. Eight patients took the medication twice daily, five patients once daily, and three thrice daily.

Table 1.

Demographics of patients.

Gender (male/female) No.		6/10
Age mean (range)		47 (19–73)
Follow-up days, median (range)		50 (30–189)
Administration routes (drops/smoke) No.		8/8
Diagnosis (number, %)	Fibromyalgia	4 (25%)
	Back pain	3 (18.75%)
	Crohn’s	2 (12.5%)
	Joint pain	3 (18.75%)
	Other	2 (12.5%)

The therapeutic effect and adherence to medical cannabis when using the app

A high engagement rate was associated with the use of the app. Figure 1 shows the overall engagement of chronic pain patients with the app. Patients were highly compliant when using the app each day at least once. The app was used at least once daily by 50% (n = 8) of patients. Four patients (25%) had a compliance rate of over 60%, one patient (6.25%) had a compliance rate of 51%, and three patients (18.75%) had a compliance rate between 40% and 50%. The remaining patients had a compliance rate between 10% and 40%. The mean compliance was 40% (range 13%-83%).

Figure 1.

The use of digital medical cannabis leads to better therapy adherence. An analysis of the data from patients using digital medical cannabis shows the rate of treatment adherence.

Chronic pain patients’ clinical outcomes were improved by introducing variability in dosing and administration times in some subjects. Examples of patients’ daily pain scores using the digital medical cannabis app can be seen in Figure 2. Although this is not a controlled study, the app-based regimen data suggested that some patients reported improved pain scores, which further motivated adherence to the treatment.

Figure 2.

Examples of how digital medical cannabis can help alleviate chronic pain include patient-reported pain scores collected retrospectively. Each panel in the data represents an individual patient. (a) A favorable and sustained clinical effect was observed within a few weeks of use. (b and c) There is variability in the clinical effects experienced by different patients.

Discussion

Despite the increasing use of medical cannabis for multiple purposes, low adherence and loss of response remain significant challenges for its chronic use.⁷ The preliminary data presented here show that digital medical cannabis, an app-regulated personalized therapeutic regimen, may overcome some challenges associated with medical cannabis. The second-generation AI system-based app regulates the use of all types of cannabis products. The system controls doses and times of administration utilizing a variability-based algorithm. While this study is not a controlled trial, the preliminary data may suggest that this system improves adherence and clinical outcomes. Combining drugs with devices may enhance adherence and outcomes, motivating patients to follow their treatment regimens.

With the legalization and increased use of medical cannabis, various products became available. The pharmacokinetics of cannabis formulations vary markedly between and within subjects.⁴¹ Although cannabis is a highly personalized medicine that requires titration, there is no method to personalize the therapy. Most therapeutic regimens are selected based on trial and error rather than validated data. There is a great deal of difficulty for physicians and patients in maximizing the benefits of these products.

Using medical cannabis for an extended period is difficult due to low adherence. In a population-based study of 5452 new users, the median use duration was 31 days, and only 18% used cannabis for 1 year.⁴² A survey of 4000 subjects consuming cannabis at least once in the past year classified the subjects into four groups based on the number of usage days per year as follows: infrequent users (<11 days), occasional users (11–50 days), regular users (51–250 days), and intensive users (>250 days).⁴³ Additionally, clinicians fail to follow prescription guidelines, complicating standardizing therapeutic regimens.⁴⁴

A common problem with chronic drugs is a loss of response to them.¹⁶ As a result of repeated exposures to cannabis, tolerance and effectiveness are reduced.^45–47 As a result of chronic use, tolerance develops, leading to a partial or complete loss of the clinical effect.^47–49

A randomized, placebo-controlled crossover study assessed how cannabis affects the brain in occasional and chronic users and found a pharmacodynamic mechanism for tolerance development.^45,47 Occasional users displayed significant neurometabolic changes in reward circuitry, decreased functional connectivity, increased striatal glutamate concentrations, and increased subjective high. Chronic cannabis users did not appear to experience similar changes, suggesting that the reward circuitry is less responsive to cannabis intoxication than chronic cannabis users.^45,47

It has been proposed that the biological response to medical cannabis takes on a U-shape.⁵⁰ Titration should begin low and be gradual to achieve better results. When a dose is raised beyond a certain level, there is a general tendency to induce tolerance, a loss of effect, and more adverse effects.^49,51,52

The first generation of AI primarily focuses on clinical decision-making through big data analysis. As a result, their real-world use is limited since most algorithms do not necessarily lead to better outcomes for patients.^53–56 These algorithms may be affected by biases resulting from using big data, which may negatively influence the overall outcome of this analysis. The engagement rate of patients and physicians in medication reminder systems is low.^57–60 Using mobile phones to remind patients to take their medications is insufficient to improve adherence to medication.^61–63

Second-generation AI systems based on CDP focus on improving patient outcomes through subject-specific AI.¹¹ Their development evolved from biological systems relying on regulated noise levels to function.^{13,32,64–72} By overcoming the compensatory mechanisms associated with tolerance and disease progression, focusing on patients’ clinical benefits ensures enhanced adherence and sustainable response to chronic drugs.^16,18–20 An algorithm based on individualized variability signatures is introduced into these systems to enhance the efficiency of chronic drugs individually. The response to chronic therapies is improved by intermittent dose adjustments and drug holidays.^{7,10,11,13,16–39,73–89}

Digital medical cannabis is provided to subjects through a user-friendly app downloaded to their cell phones.⁷ It allows the individual to follow a personalized therapeutic plan that enhances adherence and may improve responses to cannabis products by reducing tolerance and improving clinically meaningful outcomes, thereby ensuring greater engagement from both patients and physicians.^7,16,18–20

Three steps are involved in implementing digital medical cannabis. As a first level, an open-loop system was utilized on the subjects in the present study to provide random dosing and administration times within a predefined therapeutic range.¹⁹ Secondly, a closed loop is implemented, where dosages and administration times are personalized based on patient and provider feedback. In the third level, variability signatures, such as heart rate variability and cytokine secretion variability, are quantified and integrated into the algorithm.^{10,11,16–20,73,84}

One limitation of the current data analysis is that it is based on a small, uncontrolled group of participants and has a relatively short follow-up period. A controlled study, on the other hand, would include a control group that uses an app to remind them to take medical cannabis according to a regular schedule, and it would involve a longer follow-up period.

In summary, this preliminary analysis of real-world data suggests that using a second-generation personalized AI algorithm to randomize cannabis regimens could improve clinical responses and treatment adherence. Digital medical cannabis has the potential to enhance clinical effectiveness and reduce tolerance to cannabis products, which may lead to lower dosages and fewer side effects. However, these findings need to be validated through extensive, prospective controlled studies.

Footnotes

Abbreviations

AI: artificial intelligence; CDP: constrained disorder principle; THC: Delta9-tetrahydrocannabinol; CBD: Cannabidiol;

ORCID iD

Yaron Ilan

Author contributions

NH, HL, YH, and YK collected the data; KJ, AS, and MB provided the platform; SA and YI conceptualized. All authors reviewed the final version.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: YI is the founder of Oberon Sciences, SA is a consultant for Oberon Sciences, MB is a consultant for Area9, and KJ is an employee of Area9.

Data availability statement

Upon request.

References

McDonagh

Morasco

Wagner

, et al. Cannabis-based products for chronic pain : a systematic review. Ann Intern Med 2022; 175: 1143–1153.

Greywoode

Cunningham

Hollins

, et al. Medical cannabis use patterns and adverse effects in inflammatory bowel disease. J Clin Gastroenterol 2022; 57: 824–829.

Ilnytskyy

Ghasemi Gojani

, et al. Analysis of anti-cancer and anti-inflammatory properties of 25 High-THC cannabis extracts. Molecules 2022; 27(18): 6057. DOI: 10.3390/molecules27186057.

Mondino

Cavelli

González

, et al. Effects of cannabis consumption on sleep. Adv Exp Med Biol 2021; 1297: 147–162.

Babayeva

Assefa

Basu

, et al. Autism and associated disorders: cannabis as a potential therapy. Front Biosci 2022; 14: 1.

Bar-Lev Schleider

Mechoulam

Sikorin

, et al. Adherence, safety, and effectiveness of medical cannabis and epidemiological characteristics of the patient population: a prospective study. Front Med 2022; 9: 827849.

Ilan

Digital Medical Cannabis as market differentiator: second-generation artificial intelligence systems to improve response. Front Med 2021; 8: 788777.

Ransing

de la Rosa

Pereira-Sanchez

, et al. Current state of cannabis use, policies, and research across sixteen countries: cross-country comparisons and international perspectives. Trends Psychiatry Psychother 2022; 44: e20210263.

Carter

El-Den

The impact of mobile applications on medication adherence: a systematic review. Transl Behav Med 2020; 10: 1419–1435.

10.

Bayatra

Nasserat

Ilan

Overcoming low adherence to chronic medications by improving their effectiveness using a personalized second-generation digital system. Curr Pharm Biotechnol 2024; 25: 2078–2088.

11.

Ilan

The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems. Comput Struct Biotechnol J 2022; 20: 6087–6096.

12.

Ilan

Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function. Mol Cell Biochem 2022; 478: 375–392.

13.

Ilan

Making use of noise in biological systems. Prog Biophys Mol Biol 2023; 178: 83–90.

14.

Ilan

Constrained disorder principle-based variability is fundamental for biological processes: beyond biological relativity and physiological regulatory networks. Prog Biophys Mol Biol 2023; 180–181: 37–48.

15.

Ilan

The constrained-disorder principle defines the functions of systems in nature. Front Netw Physiol 2024; 4: 1361915.

16.

Ilan

Overcoming compensatory mechanisms toward chronic drug administration to ensure long-term, sustainable beneficial effects. Mol Ther Methods Clin Dev 2020; 18: 335–344.

17.

Hurvitz

Ilan

The constrained-disorder principle assists in overcoming significant challenges in digital health: moving from “nice to have” to mandatory systems. Clin Pract 2023; 13: 994–1014.

18.

Ilan

Second-generation digital health platforms: placing the patient at the center and focusing on clinical outcomes. Front Digit Heal 2020; 2: 569178.

19.

Ilan

Improving global healthcare and reducing costs using second-generation artificial intelligence-based digital pills: a market disruptor. Int J Environ Res Public Health 2021; 18(2): 811. DOI: 10.3390/ijerph18020811.

20.

Ilan

Next-generation personalized medicine: implementation of variability patterns for overcoming drug resistance in chronic diseases. J Pers Med 2022; 12(8): 1303. DOI: 10.3390/jpm12081303.

21.

Kessler

Weksler-Zangen

Ilan

Role of the immune system and the circadian rhythm in the pathogenesis of chronic pancreatitis: establishing a personalized signature for improving the effect of immunotherapies for chronic pancreatitis. Pancreas 2020; 49: 1024–1032.

22.

Ishay

Kolben

Kessler

, et al. Role of circadian rhythm and autonomic nervous system in liver function: a hypothetical basis for improving the management of hepatic encephalopathy. Am J Physiol Gastrointest Liver Physiol 2021; 321: G400–G412.

23.

Kolben

Weksler-Zangen

Ilan

Adropin as a potential mediator of the metabolic system-autonomic nervous system-chronobiology axis: implementing a personalized signature-based platform for chronotherapy. Obes Rev 2021; 22: e13108.

24.

Kenig

Kolben

Asleh

, et al. Improving diuretic response in heart failure by implementing a patient-tailored variability and chronotherapy-guided algorithm. Front Cardiovasc Med 2021; 8: 695547.

25.

Azmanov

Ross

Ilan

Establishment of an individualized chronotherapy, autonomic nervous system, and variability-based dynamic platform for overcoming the loss of response to analgesics. Pain Physician 2021; 24: 243–252.

26.

Potruch

Khoury

Ilan

The role of chronobiology in drug-resistance epilepsy: the potential use of a variability and chronotherapy-based individualized platform for improving the response to anti-seizure drugs. Seizure 2020; 80: 201–211.

27.

Isahy

Ilan

Improving the long-term response to antidepressants by establishing an individualized platform based on variability and chronotherapy. Int J Clin Pharmacol Ther 2021; 59: 768–774.

28.

Khoury

Ilan

Introducing patterns of variability for overcoming compensatory adaptation of the immune system to immunomodulatory agents: a novel method for improving clinical response to anti-TNF therapies. Front Immunol 2019; 10: 2726.

29.

Khoury

Ilan

Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity. Obes Res Clin Pract 2021; 15: 114–123.

30.

Kenig

Ilan

A personalized signature and chronotherapy-based platform for improving the efficacy of sepsis treatment. Front Physiol 2019; 10: 1542.

31.

Ilan

Why targeting the microbiome is not so successful: can randomness overcome the adaptation that occurs following gut manipulation?

Clin Exp Gastroenterol 2019; 12: 209–217.

32.

Ilan

Beta-glycosphingolipids as mediators of both inflammation and immune tolerance: a manifestation of randomness in biological systems. Front Immunol 2019; 10: 1143.

33.

Gelman

Bayatra

Kessler

, et al. Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: an algorithm-based method for overcoming resistance to antiviral agents. Emerg Microbes Infect 2020; 9: 1397–1406.

34.

Ishay

Potruch

Schwartz

, et al. A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: an adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage. Biomed Pharmacother 2021; 143: 112228.

35.

Ilan

Spigelman

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: using the inherent trajectories which underlie cancer for overcoming drug resistance. Cancer Treat Res Commun 2020; 25: 100240.

36.

Hurvitz

Azmanov

Kesler

, et al. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 2021; 29: 1485–1490.

37.

Gelman

Berg

Ilan

A subject-tailored variability-based platform for overcoming the plateau effect in sports training: a narrative review. Int J Environ Res Public Health 2022; 19(3): 1722. DOI: 10.3390/ijerph19031722.

38.

Azmanov

Bayatra

Ilan

Digital analgesic comprising a second-generation digital health system: increasing effectiveness by optimizing the dosing and minimizing side effects. J Pain Res 2022; 15: 1051–1060.

39.

Hurvitz

Elkhateeb

Sigawi

, et al. Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms. Front Aging 2022; 3: 1044038.

40.

Schoeler

Petros

Di Forti

, et al. Poor medication adherence and risk of relapse associated with continued cannabis use in patients with first-episode psychosis: a prospective analysis. Lancet Psychiatry 2017; 4: 627–633.

41.

Sempio

Huestis

Mikulich-Gilbertson

, et al. Population pharmacokinetic modeling of plasma Delta9-tetrahydrocannabinol and an active and inactive metabolite following controlled smoked cannabis administration. Br J Clin Pharmacol 2020; 86: 611–619.

42.

Alkabbani

Marrie

Bugden

, et al. Persistence of use of prescribed cannabinoid medicines in Manitoba, Canada: a population-based cohort study. Addiction 2019; 114: 1791–1799.

43.

van Laar

Frijns

Trautmann

, et al. Sizing the cannabis market: a demand-side and user-specific approach in seven European countries. Curr Drug Abuse Rev 2013; 6: 152–164.

44.

Elias

MacLaren

Hill-Elias

, et al. Adherence to prescription guidelines for medical cannabis in disability claimants. Can Fam Physician 2019; 65: e339–e343.

45.

Mason

Theunissen

Hutten

NRPW

, et al. Reduced responsiveness of the reward system is associated with tolerance to cannabis impairment in chronic users. Addict Biol 2019; 26: e12870.

46.

Ramaekers

Mason

Theunissen

EL.

Blunted highs: Pharmacodynamic and behavioral models of cannabis tolerance. Eur Neuropsychopharmacol 2020; 36: 191–205.

47.

Schlag

Hindocha

Zafar

, et al. Cannabis based medicines and cannabis dependence: a critical review of issues and evidence. J Psychopharmacol 2021; 35: 773–785.

48.

Colizzi

Bhattacharyya

Cannabis use and the development of tolerance: a systematic review of human evidence. Neurosci Biobehav Rev 2018; 93: 1–25.

49.

Gorelick

Goodwin

Schwilke

, et al. Tolerance to effects of high-dose oral delta9-tetrahydrocannabinol and plasma cannabinoid concentrations in male daily cannabis smokers. J Anal Toxicol 2013; 37: 11–16.

50.

Linares

Zuardi

Pereira

, et al. Cannabidiol presents an inverted U-shaped dose-response curve in a simulated public speaking test. Braz J Psychiatry 2019; 41: 9–14.

51.

MacCallum

Russo

EB.

Practical considerations in medical cannabis administration and dosing. Eur J Intern Med 2018; 49: 12–19.

52.

Bhaskar

Bell

Boivin

, et al. Consensus recommendations on dosing and administration of medical cannabis to treat chronic pain: results of a modified Delphi process. J Cannabis Res 2021; 3: 22.

53.

Briganti

Le Moine

Artificial intelligence in medicine: today and tomorrow. Front Med 2020; 7: 27.

54.

Overley

Cho

Mehta

, et al. Navigation and robotics in spinal surgery: where are we now? Neurosurg 2017; 80: S86–S99.

55.

Tepper

Rudy

Lefkowitz

, et al. Mixed reality with HoloLens: where virtual reality meets augmented reality in the operating room. Plast Reconstr Surg 2017; 140: 1066–1070.

56.

Malloy

Milling

LS.

The effectiveness of virtual reality distraction for pain reduction: a systematic review. Clin Psychol Rev 2010; 30: 1011–1018.

57.

Dahlhausen

Zinner

Bieske

, et al. There’s an app for that, but nobody's using it: Insights on improving patient access and adherence to digital therapeutics in Germany. Digit Health 2022; 8: 20552076221104672.

58.

Jezrawi

Balakumar

Masud

, et al. Patient and physician perspectives on the use and outcome measures of mHealth apps: exploratory survey and focus group study. Digit Health 2022; 8: 20552076221102773.

59.

Lee

Yoon

SN.

Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health 2021; 18(1): 271. DOI: 10.3390/ijerph18010271.

60.

Davenport

Kalakota

The potential for artificial intelligence in healthcare. Future Healthc J 2019; 6: 94–98.

61.

Demena

Artavia-Mora

Ouedraogo

, et al. A systematic review of mobile phone interventions (SMS/IVR/Calls) to improve adherence and retention to antiretroviral treatment in low-and middle-income countries. AIDS Patient Care STDS 2020; 34: 59–71.

62.

Al-Arkee

Mason

Lane

, et al. Mobile apps to improve medication adherence in cardiovascular disease: systematic review and meta-analysis. J Med Internet Res 2021; 23: e24190.

63.

Vervloet

Linn

van Weert

, et al. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc 2012; 19: 696–704.

64.

Ilan

Overcoming randomness does not rule out the importance of inherent randomness for functionality. J Biosci 2019; 44(6): 132.

65.

Ilan

Generating randomness: making the most out of disordering a false order into a real one. J Transl Med 2019; 17: 49.

66.

Ilan

Advanced tailored randomness: a novel approach for improving the efficacy of Biological Systems. J Comput Biol 2020; 27: 20–29.

67.

Ilan

Order through disorder: the characteristic variability of systems. Front Cell Dev Biol 2020; 8: 186.

68.

El-Haj

Kanovitch

Ilan

Personalized inherent randomness of the immune system is manifested by an individualized response to immune triggers and immunomodulatory therapies: a novel platform for designing personalized immunotherapies. Immunol Res 2019; 67: 337–347.

69.

Ilan

Randomness in microtubule dynamics: an error that requires correction or an inherent plasticity required for normal cellular function?

Cell Biol Int 2019; 43: 739–748.

70.

Ilan

Microtubules: from understanding their dynamics to using them as potential therapeutic targets. J Cell Physiol 2019; 234: 7923–7937.

71.

Ilan-Ber

Ilan

The role of microtubules in the immune system and as potential targets for gut-based immunotherapy. Mol Immunol 2019; 111: 73–82.

72.

Forkosh

Kenig

Ilan

Introducing variability in targeting the microtubules: review of current mechanisms and future directions in colchicine therapy. Pharmacol Res Perspect 2020; 8: e00616.

73.

Sigawi

Ilan

Using constrained-disorder principle-based systems to improve the performance of Digital Twins in biological systems. Biomimetics 2023; 8(4): 359. DOI: 10.3390/biomimetics8040359.

74.

Kolben

Azmanov

Gelman

, et al. Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections. Ann Med 2023; 55: 311–318.

75.

Lehmann

Arkadir

Ilan

Methods for improving brain-computer interface: using a brain-directed adjuvant and a second-generation artificial intelligence system to enhance information streaming and effectiveness of stimuli. Int J Appl Biol Pharm Technol 2023; 14: 42–52.

76.

Adar

Hollander

Ilan

The constrained disorder principle accounts for the variability that characterizes breathing: a method for treating chronic respiratory diseases and improving mechanical ventilation. Adv Respir Med 2023; 91: 350–367.

77.

Ilan

The constrained disorder principle accounts for the structure and function of water as an ultimate biosensor and bioreactor in biological systems. Int J Appl Biol Pharm Technol 2023; 14: 31–41.

78.

Sigawi

Hamtzany

Shakargy

, et al. The constrained disorder principle may account for consciousness. Brain Sci 2024; 14(3): 209. DOI: 10.3390/brainsci14030209.

79.

Ilan

Special Issue “computer-aided drug discovery and treatment. Int J Mol Sci 2024; 25(5): 2683. DOI: 10.3390/ijms25052683.

80.

Hurvitz

Dinur

Revel-Vilk

, et al. A feasibility open-labeled clinical trial using a second-generation artificial-intelligence-based therapeutic regimen in patients with Gaucher disease treated with enzyme replacement therapy. J Clin Med 2024; 13(11): 3325. DOI: 10.3390/jcm13113325.

81.

Ilan

Free will as defined by the constrained disorder principle: a restricted, mandatory, personalized, regulated process for decision-making. Integr Psychol Behav Sci 2024; 58: 1843–1875.

82.

Ilan

The constrained disorder principle defines mitochondrial variability and provides a platform for a novel mechanism for improved functionality of complex systems. Fortune J Health Sci 2024; 7: 338–347.

83.

Sigawi

Israeli

Ilan

Harnessing variability signatures and biological noise may enhance immunotherapies' efficacy and act as novel biomarkers for diagnosing and monitoring immune-associated disorders. ImmunoTargets Ther 2024; 13: 525–539.

84.

Sigawi

Lehmann

Hurvitz

, et al. Constrained Disorder Principle-based second-generation algorithms implement quantified variability signatures to improve the function of complex systems. J Bioinform Syst Biol 2023; 6: 82–89.

85.

Gelman

Hurvitz

Nesserat

, et al. A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: results of a feasibility open-labeled clinical trial. Biomed Pharmacother 2023; 161: 114334.

86.

Sigawi

Gelman

Maimon

, et al. Improving the response to lenvatinib in partial responders using a Constrained-Disorder-Principle-based second-generation artificial intelligence-therapeutic regimen: a proof-of-concept open-labeled clinical trial. Front Oncol 2024; 14: 1426426.

87.

Ilan

Using the Constrained Disorder Principle to navigate uncertainties in biology and medicine: refining fuzzy algorithms. Biology 2024; 13(10): 830. DOI: 10.3390/biology13100830.

88.

Ilan

The constrained disorder principle overcomes the challenges of methods for assessing uncertainty in biological systems. J Pers Med 2025; 15: 10.

89.

Ilan

The co-piloting model for using artificial intelligence systems in medicine: implementing the constrained-disorder-principle-based second-generation system. Bioengineering 2024; 11: 1111.

A constrained disorder principle-based second-generation artificial intelligence digital medical cannabis system: A real-world data analysis

Abstract

Introduction:

Aim:

Design and methods:

Results:

Summary:

Keywords

Relevance for public health

Introduction

Design and methods

Results

Subjects

The therapeutic effect and adherence to medical cannabis when using the app

Discussion

Footnotes

Abbreviations

ORCID iD

Author contributions

Funding

Declaration of conflicting interests

Data availability statement

References