Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. Healthcare fusion is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 179–192]. Data science and process improvement are also expected to play a role in resource planning and operational efficiency for optimal patient-centered care. Driving this transformation are advances in ‘omics’ technologies, digital devices, and imaging capabilities, along with an arsenal of powerful analytics tools working across a multitude of institutions and stakeholders. Encompassing this entire ecosystem, medicine will be evidence-based and driven by three key components: (1) Data curation through clinical diagnostics and behavioral apps that capture health and disease states; (2) Individualized solutions driven by advanced data analytics and personalized therapies; and (3) Business models that deliver value and incentivize growth. The aim of this paper is to present a novel conceptual framework to leverage AI and enhance information flow to serve the patient as per components one and two.

Metronomic chemotherapy (MCT), characterized by the continuous administration of chemotherapeutics at a lower dose without prolonged drug-free periods, has garnered significant attention over the last 2 decades. Extensive evidence from both pre-clinical and clinical settings indicates that MCT induces distinct biological effects than the standard Maximum Tolerated Dose (MTD) chemotherapy. The low toxicity profile, reduced likelihood of inducing acquired therapeutic resistance, and low cost of MCT render it an attractive chemotherapeutic regimen option. One of the most prominent aspects of MCT is its anti-angiogenesis effects. It has been shown to stimulate the expression of anti-angiogenic molecules, thereby inhibiting angiogenesis. In addition, MCT has been shown to decrease the regulatory T-cell population and promote anti-tumor immune response through inducing dendritic cell maturation and increasing the number of cytotoxic T-cells. Combination therapies utilizing MCT along with oncolytic virotherapy, radiotherapy or other chemotherapeutic regimens have been studied extensively. This review provides an overview of the current status of MCT research and the established mechanisms of action of MCT treatment and also offers insights into potential avenues of development for MCT in the future.

Immunotherapy is now established as a potent therapeutic paradigm engendering antitumor immune response against a wide range of malignancies and other diseases by modulating the immune system either through the stimulation or suppression of immune components such as CD4⁺ T cells, CD8⁺ T cells, B cells, monocytes, macrophages, dendritic cells, and natural killer cells. By targeting several immune checkpoint inhibitors or blockers (e.g., PD-1, PD-L1, PD-L2, CTLA-4, LAG3, and TIM-3) expressed on the surface of immune cells, several monoclonal antibodies and polyclonal antibodies have been developed and already translated clinically. In addition, natural killer cell-based, dendritic cell-based, and CAR T cell therapies have been also shown to be promising and effective immunotherapeutic approaches. In particular, CAR T cell therapy has benefited from advancements in CRISPR-Cas9 genome editing technology, allowing the generation of several modified CAR T cells with enhanced antitumor immunity. However, the emerging SARS-CoV-2 infection could hijack a patient’s immune system by releasing pro-inflammatory interleukins and cytokines such as IL-1β, IL-2, IL-6, and IL-10, and IFN-γ and TNF-α, respectively, which can further promote neutrophil extravasation and the vasodilation of blood vessels. Despite the significant development of advanced immunotherapeutic technologies, after a certain period of treatment, cancer relapses due to the development of resistance to immunotherapy. Resistance may be primary (where tumor cells do not respond to the treatment), or secondary or acquired immune resistance (where tumor cells develop resistance gradually to ICIs therapy). In this context, this review aims to address the existing immunotherapeutic technologies against cancer and the resistance mechanisms against immunotherapeutic drugs, and explain the impact of COVID-19 on cancer treatment. In addition, we will discuss what will be the future implementation of these strategies against cancer drug resistance. Finally, we will emphasize the practical steps to lay the groundwork for enlightened policy for intervention and resource allocation to care for cancer patients.

As next-generation sequencing (NGS) has become more widely used, germline and rare genetic variations responsible for inherited illnesses, including cancer predisposition syndromes (CPSs) that account for up to 10% of childhood malignancies, have been found. The CPSs are a group of germline genetic disorders that have been identified as risk factors for pediatric cancer development. Excluding a few “classic” CPSs, there is no agreement regarding when and how to conduct germline genetic diagnostic studies in children with cancer due to the constant evolution of knowledge in NGS technologies. Various clinical screening tools have been suggested to aid in the identification of individuals who are at greater risk, using diverse strategies and with varied outcomes. We present here an overview of the primary clinical and molecular characteristics of various CPSs and summarize the existing clinical genomics data on the prevalence of CPSs in pediatric cancer patients. Additionally, we discuss several ethical issues, challenges, limitations, cost-effectiveness, and integration of genomic newborn screening for CPSs into a healthcare system. Furthermore, we assess the effectiveness of commonly utilized decision-support tools in identifying patients who may benefit from genetic counseling and/or direct genetic testing. This investigation highlights a tailored and systematic approach utilizing medical newborn screening tools such as the genome sequencing of high-risk newborns for CPSs, which could be a practical and cost-effective strategy in pediatric cancer care.

A “quiet revolution” in medicine has been taking place over the past two decades. There are two converging dynamic forces that have propelled precision medicine to the limelight, garnering wide public attention. The first driver is the realization that populations within a disease area can be stratified, thus developing therapies tailored to their specific needs, and the capability to identify these populations by analyzing large, diverse datasets. The second driver is technology advances in multi‐omics approaches and applications (i.e., molecularly informed medicine) enabling a more comprehensive portrait of disease biology. This promises to not only accelerate the development of precision medicine processes but also presents challenges for healthcare professionals and health systems that are struggling to interconnect and integrate disparate data sources into a cohesive clinical strategy to the benefit of their patients. We coin here the term next‐generation precision medicine (ngPM), which is bound to become conventional in the clinics sooner or later. Artificial intelligence (AI) and machine learning (ML) in healthcare have transformative potential and are a strategic response to today's challenges and tomorrow's opportunities. The chief challenges here are how well precision medicine (PM) permeates primary care to become a standard of care and drive toward precision wellness or precision lifestyle (ngPM), while ensuring access to care is feasible, streamlined, and routine. We present here a perspective that would harness the power of ngPM for precision wellness.

Maternal and neonatal health (women during pregnancy, childbirth, and the postnatal period) presents a spectrum of healthcare challenges, including preterm birth, preeclampsia, intrauterine growth restriction, polycystic ovarian syndrome, and gestational diabetes mellitus. While genomic investigations have shed light on many of these topics, protein biomarker discovery, a pivotal aspect of such research, holds promise in offering insights into disease diagnosis, progression, and prognosis. This review paper aims to explore the landscape of proteomics research pertaining to the aforementioned disorders. In the search for viable biomarkers, existing ones are either outdated or lack specificity and new ones being investigated do not commonly make it to the validation stage. In this review, the reasons for the gap between the biomarker discovery stage and the clinical validation stage are evaluated, in addition to what steps are being taken to mitigate the unexpectedly slow scientific and clinical progress. Notably, this paper also delves into the ethnic disparities found in maternal and neonatal health research, as well as how AI is currently being used to alleviate socioeconomic and ethnic disparities, as well as its advantages for the analysis of large “omics” datasets. We anticipate this investigation will provide critical, invaluable information for researchers, medical professionals, and policy decision‐makers in this field to improve overall maternal and neonatal health outcomes.