Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

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Boys and pubertal adolescents are more prone to respond poorly to standard obesity care while those with greater baseline degree of obesity and carriers of the FTO obesity-predisposing allele are not.

Background: Control of chronic pain and mainly the partial or complete loss of response to analgesics is a major unmet need. Multiple mechanisms underline the development of tolerance to analgesics in general and specifically to opioids. The autonomic nervous system (ANS) plays a role in the development of analgesic tolerance and chronobiology. Objectives: To review the mechanisms associated with the development of nonresponsiveness to analgesics. Study Design: Literature review. Setting: The review is followed by a description of a new method for overcoming resistance and improving the response to analgesics. Methods: Conducted a detailed review of the relevant studies describing the mechanisms that underlie tolerance to pain medications, and the potential roles of the ANS and chronobiology in the development of drug resistance. Results: The autonomic balance is reflected by heart rate variability, an example of a fundamental variability that characterizes biological systems. Chronotherapy, which is based on the circadian rhythm, can improve the efficacy and reduce the toxicity of chronic medications. In this article, we present the establishment of an individualized variability- and chronobiology-based therapy for overcoming the compensatory mechanisms associated with a loss of response to analgesics. We describe the premise of implementing personalized signatures associated with the ANS, and chronobiology, as well as with the pathophysiology of pain for establishing an adaptive model that could improve the efficacy of opioids, in a highly dynamic system. Limitations: The studies presented were selected based on their relevance to the subject. Conclusions: The described variability-based system may ensure prolonged effects of analgesics while reducing the toxicity associated with increasing dosages. Key words: Painkillers, opioids, drug resistance, compensatory mechanisms

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients’ adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them. IMPORTANCE SECTION The paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. Key messages Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.