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

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms

Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant.

Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies' Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders

Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined and change between and among subjects, the current methods for overcoming the loss of response are insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that change in response to internal and external perturbations. Inter and intra-subject variability characterize the immune system, making it difficult to provide a single therapeutic regimen to all patients and even the same patients over time. The dynamicity of the immune variability is also a significant challenge for personalizing immunotherapies. The CDP-based second-generation artificial intelligence system is an outcome-based dynamic platform that incorporates personalized variability signatures into the therapeutic regimen and may provide methods for improving the response and overcoming the loss of response to treatments. The signatures of immune variability may also offer a method for identifying new biomarkers for early diagnosis, monitoring immune-related disorders, and evaluating the response to treatments.

Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

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

Introduction

The main obstacle in treating cancer patients is drug resistance. Lenvatinib treatment poses challenges due to loss of response and the common dose-limiting adverse events (AEs). The Constrained-disorder-principle (CDP)-based second-generation artificial intelligence (AI) systems introduce variability into treatment regimens and offer a potential strategy for enhancing treatment efficacy. This proof-of-concept clinical trial aimed to assess the impact of a personalized algorithm-controlled therapeutic regimen on lenvatinib effectiveness and tolerability.

Methods

A 14-week open-label, non-randomized trial was conducted with five cancer patients receiving lenvatinib-an AI-assisted application tailored to a personalized therapeutic regimen for each patient, which the treating physician approved. The study assessed changes in tumor response through FDG-PET-CT and tumor markers and quality of life via the EORTC QLQ-THY34 questionnaire, AEs, and laboratory evaluations. The app monitored treatment adherence.

Results

At 14 weeks of follow-up, the disease control rate (including the following outcomes: complete response, partial response, stable disease) was 80%. The FDG-PET-CT scan-based RECIST v1.1 and PERCIST criteria showed partial response in 40% of patients and stable disease in an additional 40% of patients. One patient experienced a progressing disease. Of the participants with thyroid cancer, 75% showed a reduction in thyroglobulin levels, and 60% of all the participants showed a decrease in neutrophil-to-lymphocyte ratio during treatment. Improvement in the median social support score among patients utilizing the system supports an ancillary benefit of the intervention. No grade 4 AEs or functional deteriorations were recorded.

Summary

The results of this proof-of-concept open-labeled clinical trial suggest that the CDP-based second-generation AI system-generated personalized therapeutic recommendations may improve the response to lenvatinib with manageable AEs. Prospective controlled studies are needed to determine the efficacy of this approach.

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

Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme β-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings.

Effects of a lower limb walking exoskeleton on quality of life and activities of daily living in patients with complete spinal cord injury: A randomized controlled trial

BACKGROUND

In recent years, lower limb walking exoskeletons have been widely used in the study of spinal cord injury (SCI).

OBJECTIVE

To explore the effect of a lower limb walking exoskeleton on quality of life and functional independence in patients with motor complete SCI.

METHODS

This was a multi-center, single blind, randomized controlled trial. A total of 16 SCI patients were randomly assigned to either the exoskeleton-assisted walking (EAW) group (n= 8) or the conventional group (n= 8). Both groups received conventional rehabilitation training, including aerobic exercise and strength training. The EAW group additionally conducted the exoskeleton-assisted walking training using an AIDER powered robotic exoskeleton for 40-50 minutes, 5 times/week for 8 weeks. World Health Organization quality of life-BREF (WHOQOL-BREF) and the Spinal Cord Independence Measure III (SCIM-III) were used for assessment before and after training.

RESULTS

There was an increasing tendency of scores in the psychological health, physical health, and social relationships domain of WHOQOL-BREF in the EAW group after the intervention compared with the pre-intervention period, but there was no significant difference (P> 0.05). SCIM-III scores increased in both groups compared to pre-training, with only the conventional group showing a significant difference after 8 weeks of training (P< 0.05).

CONCLUSION

A lower limb walking exoskeleton may have potential benefits for quality of life and activities of daily living in patients with motor complete SCI.

Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized Second-generation Digital System

INTRODUCTION

Low adherence to chronic treatment regimens is a significant barrier to improving clinical outcomes in patients with chronic diseases. Low adherence is a result of multiple factors.

METHODS

We review the relevant studies on the prevalence of low adherence and present some potential solutions.

RESULTS

This review presents studies on the current measures taken to overcome low adherence, indicating a need for better methods to deal with this problem. The use of first-generation digital systems to improve adherence is mainly based on reminding patients to take their medications, which is one of the reasons they fail to provide a solution for many patients. The establishment of a second-generation artificial intelligence system, which aims to improve the effectiveness of chronic drugs, is described.

CONCLUSION

Improving clinically meaningful outcome measures and disease parameters may increase adherence and improve patients' response to therapy.

Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stable vs. unstable PA ; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stable vs. unstable PA ; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stable vs. unstable PA ; P=0.02; adjusted P=0.04) and improved short-term memory (score change: -0.28 vs. +0.29, stable vs. unstable PA ; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

INTRODUCTION

Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens.

METHODS

Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function.

RESULTS

The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations.

SUMMARY

The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.