Hasil untuk "Public aspects of medicine"

Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan et al.

Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Hongting Wang, Ying Chen, Lei Wang et al.

This manuscript provides an in-depth review of the significance of quality control in herbal medication products, focusing on its role in maintaining efficiency and safety. With a historical foundation in traditional medicine systems, herbal remedies have gained widespread popularity as natural alternatives to conventional treatments. However, the increasing demand for these products necessitates stringent quality control measures to ensure consistency and safety. This comprehensive review explores the importance of quality control methods in monitoring various aspects of herbal product development, manufacturing, and distribution. Emphasizing the need for standardized processes, the manuscript delves into the detection and prevention of contaminants, the authentication of herbal ingredients, and the adherence to regulatory standards. Additionally, it highlights the integration of traditional knowledge and modern scientific approaches in achieving optimal quality control outcomes. By emphasizing the role of quality control in herbal medicine, this manuscript contributes to promoting consumer trust, safeguarding public health, and fostering the responsible use of herbal medication products.

C. Y. Chung, A. Chu, B. Chung

The genomics revolution over the past three decades has led to great strides in rare disease (RD) research, which presents a major shift in global policy landscape. While RDs are individually rare, there are common challenges and unmet medical and social needs experienced by the RD population globally. The various disabilities arising from RDs as well as diagnostic and treatment uncertainty were demonstrated to have detrimental influence on the health, psychosocial, and economic aspects of RD families. Despite the collective large number of patients and families affected by RDs internationally, the general lack of public awareness and expertise constraints have neglected and marginalized the RD population in health systems and in health- and social-care policies. The current Coronavirus Disease of 2019 (COVID-19) pandemic has exposed the long-standing and fundamental challenges of the RD population, and has reminded us of the critical need of addressing the systemic inequalities and widespread disparities across populations and jurisdictions. Owing to the commonality in goals between RD movements and universal health coverage targets, the United Nations (UN) has highlighted the importance of recognizing RDs in policies, and has recently adopted the UN Resolution to promote greater integration of RDs in the UN agenda, advancing UN's commitment in achieving the 2030 Sustainable Development Goals of “leav[ing] no one behind.” Governments have also started to launch Genome Projects in their respective jurisdictions, aiming to integrate genomic medicine into mainstream healthcare. In this paper, we review the challenges experienced by the RD population, the establishment and adoption of RD policies, and the state of evidence in addressing these challenges from a global perspective. The Hong Kong Genome Project was illustrated as a case study to highlight the role of Genome Projects in enhancing clinical application of genomic medicine for personalized medicine and in improving equity of access and return in global genomics. Through reviewing what has been achieved to date, this paper will provide future directions as RD emerges as a global public health priority, in hopes of moving a step toward a more equitable and inclusive community for the RD population in times of pandemics and beyond.

Anran Li, Lingfei Qian, Mengmeng Du et al.

Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety. However, a key open question remains: to what extent do LLMs memorize medical training data. Memorization can be beneficial when it enables LLMs to retain valuable medical knowledge during domain adaptation. Yet, it also raises concerns. LLMs may inadvertently reproduce sensitive clinical content (e.g., patient-specific details), and excessive memorization may reduce model generalizability, increasing risks of misdiagnosis and making unwarranted recommendations. These risks are further amplified by the generative nature of LLMs, which can not only surface memorized content but also produce overconfident, misleading outputs that may hinder clinical adoption. In this work, we present a study on memorization of LLMs in medicine, assessing its prevalence (how frequently it occurs), characteristics (what is memorized), volume (how much content is memorized), and potential downstream impacts (how memorization may affect medical applications). We systematically analyze common adaptation scenarios: (1) continued pretraining on medical corpora, (2) fine-tuning on standard medical benchmarks, and (3) fine-tuning on real-world clinical data, including over 13,000 unique inpatient records from Yale New Haven Health System. The results demonstrate that memorization is prevalent across all adaptation scenarios and significantly higher than that reported in the general domain. Moreover, memorization has distinct characteristics during continued pre-training and fine-tuning, and it is persistent: up to 87% of content memorized during continued pre-training remains after fine-tuning on new medical tasks.

Zheng Hui, Yijiang River Dong, Ehsan Shareghi et al.

As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based on the AMA Principles of Medical Ethics, the ABA Model Rules of Professional Conduct, and the CFA Institute Code of Ethics. Building on this foundation, we introduce Trident-Bench, a benchmark specifically targeting LLM safety in the legal, financial, and medical domains. We evaluated 19 general-purpose and domain-specialized models on Trident-Bench and show that it effectively reveals key safety gaps -- strong generalist models (e.g., GPT, Gemini) can meet basic expectations, whereas domain-specialized models often struggle with subtle ethical nuances. This highlights an urgent need for finer-grained domain-specific safety improvements. By introducing Trident-Bench, our work provides one of the first systematic resources for studying LLM safety in law and finance, and lays the groundwork for future research aimed at reducing the safety risks of deploying LLMs in professionally regulated fields. Code and benchmark will be released at: https://github.com/zackhuiiiii/TRIDENT

Soojong Kim, Kwanho Kim, Hye Min Kim

Machine learning methods are increasingly applied to analyze health-related public discourse based on large-scale data, but questions remain regarding their ability to accurately detect different types of health sentiments. Especially, Large Language Models (LLMs) have gained attention as a powerful technology, yet their accuracy and feasibility in capturing different opinions and perspectives on health issues are largely unexplored. Thus, this research examines how accurate the three prominent LLMs (GPT, Gemini, and LLAMA) are in detecting risk-promoting versus health-supporting sentiments across two critical public health topics: Human Papillomavirus (HPV) vaccination and heated tobacco products (HTPs). Drawing on data from Facebook and Twitter, we curated multiple sets of messages supporting or opposing recommended health behaviors, supplemented with human annotations as the gold standard for sentiment classification. The findings indicate that all three LLMs generally demonstrate substantial accuracy in classifying risk-promoting and health-supporting sentiments, although notable discrepancies emerge by platform, health issue, and model type. Specifically, models often show higher accuracy for risk-promoting sentiment on Facebook, whereas health-supporting messages on Twitter are more accurately detected. An additional analysis also shows the challenges LLMs face in reliably detecting neutral messages. These results highlight the importance of carefully selecting and validating language models for public health analyses, particularly given potential biases in training data that may lead LLMs to overestimate or underestimate the prevalence of certain perspectives.

Jing Liu, Xinxing Ren, Yanmeng Xu et al.

This study proposes and implements the first LLM agents based agentic pipeline for multi task public opinion analysis. Unlike traditional methods, it offers an end-to-end, fully automated analytical workflow without requiring domain specific training data, manual annotation, or local deployment. The pipeline integrates advanced LLM capabilities into a low-cost, user-friendly framework suitable for resource constrained environments. It enables timely, integrated public opinion analysis through a single natural language query, making it accessible to non-expert users. To validate its effectiveness, the pipeline was applied to a real world case study of the 2025 U.S. China tariff dispute, where it analyzed 1,572 Weibo posts and generated a structured, multi part analytical report. The results demonstrate some relationships between public opinion and governmental decision-making. These contributions represent a novel advancement in applying generative AI to public governance, bridging the gap between technical sophistication and practical usability in public opinion monitoring.

Alice MacLean, Annelieke Driessen, Lisa Hinton et al.

ABSTRACT Introduction Interpretations of ‘recovery’ from illness are complex and influenced by many factors, not least patient expectations and experiences. This paper examines meanings of ‘recovery’, and how it is strived towards, drawing on the example of COVID‐19 infection. Methods Drawing on qualitative interviews (n = 93) conducted in the UK between February 2021 and July 2022, we compare adults' accounts of being admitted to an Intensive Care Unit (ICU) with COVID‐19 to accounts of being ill with Long COVID, defined as ongoing symptoms for at least 12 weeks postinfection. We conducted a multi‐stage comparative analysis using Nvivo to organise and code the data. Results We identified similarities and differences in participants' descriptions of their ‘worlds of illness’. For both groups, perceptions of recovery were shaped by the novel, unknown nature of COVID‐19. Participants questioned the achievability of full restoration of prior states of health, highlighted the heterogeneity of ‘recovery trajectories’ and described the hard physical and emotional work of adjusting to changed selves. Themes that revealed differences in ‘worlds of illness’ described included the different baselines, waymarkers, and pathways of illness experiences. Differences in other people's responses to their illness were also evident. For ICU participants, hospitalisation, and especially ICU admission, conferred legitimate patient status and authenticity to their symptoms. Family, friends and healthcare professionals acknowledged their illness, celebrated their survival, and granted them latitude to recover. For Long Covid participants, their patient status often lacked comparable authenticity in others' eyes. They reported encountering a lack of recognition and understanding of their ongoing need to recover. Conclusions This study highlights how the meanings of illness ascribed by others can influence how recovery is experienced. Our findings highlight the importance of ensuring people are made to feel their illness experiences are legitimate, regardless of hospitalisation status, formal diagnosis or lack of medical knowledge and pathways. They also indicate the value of emphasising the different permutations, and lack of linearity, that recovery can take. This may help to help to guard against a lack of understanding for experiences of recovery which do not meet idealised notions. Patient or Public Contribution Both studies were guided by an advisory panel that included patient and public involvement representatives with lived experience of Intensive Care/COVID experience and Long COVID respectively. Through regular meetings with the research teams, the advisory panel had input into all aspects of the study conduct, including recruitment methods and content of the interview topic guide and feedback on preliminary analyses. The Long COVID study also included a lived experience coinvestigator who contributed to data interpretation and analysis.

Zahra Arabkermani, Roxana Sharifian, Peivand Bastani et al.

Background: Vitiligo is a prevalent skin disorder that has significant biological and social consequences for the affected individuals. Therefore, appropriate measures should be taken to diagnose this disease and treat patients, and powerful information and monitoring systems, such as a registry, are required. This study aimed to identify the design requirements for vitiligo registry in Iran.Methods: This qualitative study was conducted using a content analysis approach in 2020. In total, 9 dermatologists and health information management and medical informatics specialists working in Tehran, Shiraz, and Mashhad universities of medical sciences were interviewed. The participants were selected by a non-random purposive sampling method. The data were analyzed manually using thematic analysis approach.Results: In this study, 7 major themes and 14 sub-themes were obtained regarding vitiligo registry design requirements. The major themes included registry objectives, structure, data sources, inclusion criteria, classification system, data quality control, and data reporting.Conclusion: In total, 7 major themes and 14 sub-themes were identified to design the vitiligo registry. Developing a vitiligo registry based on these requirements could provide a better understanding of this disease, deliver high-quality services to patients across the country, and facilitate research on this disease.

Xue Yang, Kexin Huang, Dewei Yang et al.

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large‐scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields—Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence‐aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy—are discussed.

Vidya Sharma, Ramaswamy Sharma

Objectives: While several food assistance programs in the United States tackle food insecurity, a relatively new program, “Food is Medicine,” (FIM) initiated in some cities not only addresses food insecurity but also targets chronic diseases by customizing the food delivered to its recipients. This review describes federal programs providing food assistance and evaluates the various sub-programs categorized under the FIM initiative. Methods: A literature search was conducted from July 7, 2023 to November 9, 2023 using the search term, “Food is Medicine”, to identify articles indexed within three major electronic databases, PubMed, Medline, and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Eligibility criteria for inclusion were: focus on any aspect of the FIM initiative within the United States, and publication as a peer-reviewed journal article in the English language. A total of 180 articles were retrieved; publications outside the eligibility criteria and duplicates were excluded for a final list of 72 publications. Supporting publications related to food insecurity, governmental and organizational websites related to FIM and other programs discussed in this review were also included. Results: The FIM program includes medically tailored meals, medically tailored groceries, and produce prescriptions. Data suggest that it has lowered food insecurity, promoted better management of health, improved health outcomes, and has, therefore, lowered healthcare costs. Conclusions: Overall, this umbrella program is having a positive impact on communities that have been offered and participate in this program. Limitations and challenges that need to be overcome to ensure its success are discussed.

A. Illert, A. Stenzinger, M. Bitzer et al.

Naomi P O'Grady, Earnest Alexander, W. Alhazzani et al.

RATIONALE: Fever is frequently an early indicator of infection and often requires rigorous diagnostic evaluation. OBJECTIVES: This is an update of the 2008 Infectious Diseases Society of America and Society (IDSA) and Society of Critical Care Medicine (SCCM) guideline for the evaluation of new-onset fever in adult ICU patients without severe immunocompromise, now using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. PANEL DESIGN: The SCCM and IDSA convened a taskforce to update the 2008 version of the guideline for the evaluation of new fever in critically ill adult patients, which included expert clinicians as well as methodologists from the Guidelines in Intensive Care, Development and Evaluation Group. The guidelines committee consisted of 12 experts in critical care, infectious diseases, clinical microbiology, organ transplantation, public health, clinical research, and health policy and administration. All task force members followed all conflict-of-interest procedures as documented in the American College of Critical Care Medicine/SCCM Standard Operating Procedures Manual and the IDSA. There was no industry input or funding to produce this guideline. METHODS: We conducted a systematic review for each population, intervention, comparison, and outcomes question to identify the best available evidence, statistically summarized the evidence, and then assessed the quality of evidence using the GRADE approach. We used the evidence-to-decision framework to formulate recommendations as strong or weak or as best-practice statements. RESULTS: The panel issued 12 recommendations and 9 best practice statements. The panel recommended using central temperature monitoring methods, including thermistors for pulmonary artery catheters, bladder catheters, or esophageal balloon thermistors when these devices are in place or accurate temperature measurements are critical for diagnosis and management. For patients without these devices in place, oral or rectal temperatures over other temperature measurement methods that are less reliable such as axillary or tympanic membrane temperatures, noninvasive temporal artery thermometers, or chemical dot thermometers were recommended. Imaging studies including ultrasonography were recommended in addition to microbiological evaluation using rapid diagnostic testing strategies. Biomarkers were recommended to assist in guiding the discontinuation of antimicrobial therapy. All recommendations issued were weak based on the quality of data. CONCLUSIONS: The guidelines panel was able to formulate several recommendations for the evaluation of new fever in a critically ill adult patient, acknowledging that most recommendations were based on weak evidence. This highlights the need for the rapid advancement of research in all aspects of this issue—including better noninvasive methods to measure core body temperature, the use of diagnostic imaging, advances in microbiology including molecular testing, and the use of biomarkers.

B. Natterson-Horowitz, A. Aktipis, Molly M Fox et al.

Evolutionary medicine – i.e. the application of insights from evolution and ecology to biomedicine – has tremendous untapped potential to spark transformational innovation in biomedical research, clinical care and public health. Fundamentally, a systematic mapping across the full diversity of life is required to identify animal model systems for disease vulnerability, resistance, and counter-resistance that could lead to novel clinical treatments. Evolutionary dynamics should guide novel therapeutic approaches that target the development of treatment resistance in cancers (e.g., via adaptive or extinction therapy) and antimicrobial resistance (e.g., via innovations in chemistry, antimicrobial usage, and phage therapy). With respect to public health, the insight that many modern human pathologies (e.g., obesity) result from mismatches between the ecologies in which we evolved and our modern environments has important implications for disease prevention. Life-history evolution can also shed important light on patterns of disease burden, for example in reproductive health. Experience during the COVID-19 (SARS-CoV-2) pandemic has underlined the critical role of evolutionary dynamics (e.g., with respect to virulence and transmissibility) in predicting and managing this and future pandemics, and in using evolutionary principles to understand and address aspects of human behavior that impede biomedical innovation and public health (e.g., unhealthy behaviors and vaccine hesitancy). In conclusion, greater interdisciplinary collaboration is vital to systematically leverage the insight-generating power of evolutionary medicine to better understand, prevent, and treat existing and emerging threats to human, animal, and planetary health.

Sibo Wei, Xueping Peng, Yi-Fei Wang et al.

The surge of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of specialized, high-quality corpora. To this end, in this paper we propose BianCang, a TCM-specific LLM, using a two-stage training process that first injects domain-specific knowledge and then aligns it through targeted stimulation to enhance diagnostic and differentiation capabilities. Specifically, we constructed pre-training corpora, instruction-aligned datasets based on real hospital records, and the ChP-TCM dataset derived from the Pharmacopoeia of the People's Republic of China. We compiled extensive TCM and medical corpora for continual pre-training and supervised fine-tuning, building a comprehensive dataset to refine the model's understanding of TCM. Evaluations across 11 test sets involving 31 models and 4 tasks demonstrate the effectiveness of BianCang, offering valuable insights for future research. Code, datasets, and models are available on https://github.com/QLU-NLP/BianCang.

Ahmed Abdeen Hamed, Tamer E. Fandy

The objective of this research is to introduce a network specialized in predicting drugs that can be repurposed by investigating real-world evidence sources, such as clinical trials and biomedical literature. Specifically, it aims to generate drug combination therapies for complex diseases (e.g., cancer, Alzheimer's). We present a multilayered network medicine approach, empowered by a highly configured ChatGPT prompt engineering system, which is constructed on the fly to extract drug mentions in clinical trials. Additionally, we introduce a novel algorithm that connects real-world evidence with disease-specific signaling pathways (e.g., KEGG database). This sheds light on the repurposability of drugs if they are found to bind with one or more protein constituents of a signaling pathway. To demonstrate, we instantiated the framework for breast cancer and found that, out of 46 breast cancer signaling pathways, the framework identified 38 pathways that were covered by at least two drugs. This evidence signals the potential for combining those drugs. Specifically, the most covered signaling pathway, ID hsa:2064, was covered by 108 drugs, some of which can be combined. Conversely, the signaling pathway ID hsa:1499 was covered by only two drugs, indicating a significant gap for further research. Our network medicine framework, empowered by GenAI, shows promise in identifying drug combinations with a high degree of specificity, knowing the exact signaling pathways and proteins that serve as targets. It is noteworthy that ChatGPT successfully accelerated the process of identifying drug mentions in clinical trials, though further investigations are required to determine the relationships among the drug mentions.

Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin et al.

Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes.

R. J. Fredericksen, R. Baker, A. Sibley et al.

Abstract Background In recent years, stimulant use has increased among persons who use opioids in the rural U.S., leading to high rates of overdose and death. We sought to understand motivations and contexts for stimulant use among persons who use opioids in a large, geographically diverse sample of persons who use drugs (PWUD) in the rural settings. Methods We conducted semi-structured individual interviews with PWUD at 8 U.S. sites spanning 10 states and 65 counties. Content areas included general substance use, injection drug use, changes in drug use, and harm reduction practices. We used an iterative open-coding process to comprehensively itemize and categorize content shared by participants related to concurrent use. Results We interviewed 349 PWUD (64% male, mean age 36). Of those discussing current use of stimulants in the context of opioid use (n = 137, 39%), the stimulant most used was methamphetamine (78%) followed by cocaine/crack (26%). Motivations for co-use included: 1) change in drug markets and cost considerations; 2) recreational goals, e.g., seeking stronger effects after heightened opioid tolerance; 3) practical goals, such as a desire to balance or alleviate the effects of the other drug, including the use of stimulants to avoid/reverse opioid overdose, and/or control symptoms of opioid withdrawal; and 4) functional goals, such as being simultaneously energized and pain-free in order to remain productive for employment. Conclusion In a rural U.S. cohort of PWUD, use of both stimulants and opioids was highly prevalent. Reasons for dual use found in the rural context compared to urban studies included changes in drug availability, functional/productivity goals, and the use of methamphetamine to offset opioid overdose. Education efforts and harm reduction services and treatment, such as access to naloxone, fentanyl test strips, and accessible drug treatment for combined opioid and stimulant use, are urgently needed in the rural U.S. to reduce overdose and other adverse outcomes.

Ge Zhu

Abstract Background With the global aging population, attention to the health and medical issues of older adults is increasing. By analyzing the relationship between older people's participation in outdoor activities and medical expenditure, this study aims to provide a scientific basis for improving their quality of life and reducing the medical burden. Methods Data on outdoor activity participation, medical expenditures, and relevant variables were collected through questionnaires and databases. A multi-chain mediation effect model was established to analyze the impact of outdoor activities on the medical expenditure of older people, considering mediation effects and heterogeneity. Results Results revealed that increased participation in outdoor activities among older adults correlated with lower medical expenditures. Outdoor activities positively influenced their health by improving mental health, cognition, eating habits, and activities of daily living, resulting in reduced medical expenditures. Robustness tests confirmed the consistent effect of outdoor activities on older people's medical expenditure. Conclusion These findings contribute to understanding the relationship between outdoor activities, health, and medical expenditure in older people, guiding policy formulation and interventions. Encouraging and supporting older adults in outdoor activities can enhance their quality of life and alleviate medical resource strain. The study's conclusions can also inform health promotion measures for other populations and serve as a basis for future research in this area.

Chester Yan Hao Ng, Hung Hung Bun, Yan Zhao et al.

Background The World Health Organization declared that COVID-19 is no longer a public health emergency of global concern on May 5, 2023. Post-COVID disorders are, however, becoming more common. Hence, there lies a growing need to develop safe and effective treatment measures to manage post-COVID disorders. Investigating the use of TCM medicinal foods in the long-term therapy of post-COVID illnesses may be beneficial given contemporary research’s emphasis on the development of medicinal foods. Scope and approach The use of medicinal foods for the long-term treatment of post-COVID disorders is highlighted in this review. Following a discussion of the history of the TCM “Medicine and Food Homology” theory, the pathophysiological effects of post-COVID disorders will be briefly reviewed. An analysis of TCM medicinal foods and their functions in treating post-COVID disorders will then be provided before offering some insight into potential directions for future research and application. Key findings and discussion TCM medicinal foods can manage different aspects of post-COVID disorders. The use of medicinal foods in the long-term management of post-COVID illnesses may be a safe and efficient therapy choice because they are typically milder in nature than chronic drug use. These findings may also be applied in the long-term post-disease treatment of similar respiratory disorders.