1. Development of engineering medicine (EngMed)
Medicine epitomizes humanity’s enduring quest for knowledge, striving continuously to comprehend diseases and promote health. Throughout history, medical progress has evolved through the gradual understanding and utilization of plants, animals, and minerals as therapeutic tools and diagnostic aids
[1],
[2]. Concurrently, advancements in science and technology have bolstered the knowledge and understanding of human health and disease, expanding traditional medical disciplines and paving the way for new frontiers such as evidence-based medicine, telemedicine, digital medicine, and integrated medicine. Thus, the evolution underscores the collaborative efforts across diverse disciplines and the drive to develop technologies that address clinical needs
[3],
[4].
Medical education is generally classified into basic medical and health sciences, clinical medicine, and public health (preventive medicine)
[5],
[6]. Beyond translational medicine, the direct involvement of engineering and technology transforms research findings of basic medical and health sciences into practical applications in clinical medicine and public health
[7]. Addressing clinical challenges requires the integration of existing and pioneering methods. Internationally, tertiary teaching hospitals affiliated with research universities exemplify successful models where clinical scientists collaborate closely with clinicians to conduct disease-focused studies and devise novel diagnostic and therapeutic strategies
[8]. The growing number of full-time clinical scientists in hospitals is advancing academic research in China. However, the integration of research and clinical practice still lags behind international standards.
Clinical engineers play a pivotal role in hospitals by managing and maintaining medical instruments, equipment, and systems to modernize facilities and ensure cost-effective operations
[9]. They are increasingly collaborating closely with clinicians, engaging in clinical research, and focusing on specific medical challenges. Their deep mechanistic insights have significantly contributed to the development of new medical technologies and materials
[10]. These collaborative efforts with clinical scientists have accelerated research and technology in clinical medicine, giving rise to a burgeoning interdisciplinary field known as EngMed
[11]. This article introduces EngMed by defining its scope and primary objectives and exploring its implications and future directions.
2. Definition and main tasks of EngMed
EngMed represents a pioneering scientific paradigm that integrates the principles of clinical medicine, biomedical engineering (BME), and translational medicine
[11]. It serves as a bridge between clinical challenges and innovative tools and methodologies in BME, thus elucidating clinical issues
[12]. Furthermore, EngMed has established feedback loops between medicine and BME, fostering the continuous refinement of disease diagnosis and treatment modalities. Positioned as a forward-thinking, interdisciplinary field, EngMed synergizes with diverse disciplines to drive future advancements.
The primary objective of EngMed is to enhance medical research by integrating materials, information, data science, and intelligence. The goal is to address practical challenges in clinical medicine and disease prevention. EngMed collaborates closely with the clinical and public health sectors to conduct in-depth research on disease mechanisms, explore new diagnostic and treatment principles, and develop innovative technologies and clinical applications (
Fig. 1).
3. Connotation of EngMed
EngMed encompasses two main aspects. First, it involves the identification and elucidation of disease phenotypes and patterns using contemporary BME methods
[13]. Second, it entails innovating and refining the understanding and treatment modalities of diseases based on derived insights. EngMed is further categorized, based on specific focus areas, into imaging, organ, bioengineering, and digital fields (
Fig. 1).
3.1. Imaging EngMed
Imaging EngMed utilizes existing medical imaging technologies to enhance the understanding of disease pathogenesis and patterns and to develop new imaging technologies and methods based on these foundations. Important imaging techniques include ultrasonography, magnetic resonance imaging, and positron emission tomography (PET)/computed tomography (CT)
[14].
Medical ultrasound imaging technology, renowned for its non-invasive, radiation-free, convenient, and cost-effective nature, has been pivotal in clinical diagnosis and treatment since its inception in the 1980s
[15]. Over the past three decades, ultrasound technology has significantly evolved through innovations in medicine, computing, digital technology, electronics, materials science, and imaging algorithms
[16]. This convergence has led to substantial breakthroughs in ultrasound diagnostic equipment, expanding its clinical applications
[17]. While traditional B-mode ultrasound provides two-dimensional (2D) images of specific body cross-sections, it has inherent limitations that necessitate the mental reconstruction of three-dimensional (3D) structures from multiple 2D images, demanding extensive training and proficiency in ultrasound manipulation by clinicians
[18]. 3D ultrasound imaging was developed in the late 1990s to address these limitations and has been widely used in gynecology, obstetrics, cardiology, medical imaging, and surgical navigation through image fusion
[19],
[20]. In obstetric care, for example, 3D imaging allows intuitive visualization of fetal facial, cranial, and cardiac tissue structures, aiding in diagnosis and treatment decisions
[21].
Magnetic resonance imaging (MRI) covers nearly all organs except the lung cavities. It is versatile, spanning various anatomical regions, including the head, neck, spine, cardiovascular system, breast, abdomen, pelvis, muscles, and joints. While MRI excels at providing anatomical and structural details, clinicians often require additional dimensional information, such as metabolism, physiology, and function. Functional MRI (fMRI) was introduced to meet this need, revolutionizing the exploration of human cognition and cardiac function
[22]. Advances in big data, artificial intelligence (AI), and integrative diagnostic−therapeutic approaches have extended MRI’s reach into imaging genomics, tissue texture characterization, and cellular- and genetic-level insights, deepening the understanding of disease mechanisms and enhancing patient care
[23].
PET/CT combines molecular, metabolic, and anatomical information and is pivotal in oncology, neurology, and Cardiovascular disease (CVD) diagnostics. This technology informs treatment planning by assessing tumor malignancy, metastasis, and other critical factors
[24]. With ongoing technological advancements and clinical demands, PET/CT continues to evolve, driven by innovations in nanotechnology, AI, and the growing need for geriatric disease diagnostics, such as Alzheimer’s and Parkinson’s diseases
[25].
In summary, Imaging EngMed leverages electromagnetic, ultrasound, thermal, and other modalities to deepen our understanding of disease mechanisms, driving advancements in existing technologies and the development of novel technologies for enhanced clinical applications (
Fig. 2).
3.2. Organ EngMed
3.2.1. Vascular EngMed
CVD is a major threat to global health. According to the World Health Organization (WHO), CVD accounted for 17.9 million deaths in 2016, representing nearly one-third of all deaths that year (56.9 million deaths worldwide)
[26]. In China, CVD affected 330 million people in 2019, with over 4 million deaths per year, a 48.06% increase in the total mortality rate over the past 15 years
[27]. Addressing this challenge requires an enhanced understanding, control, and treatment of CVD
[28]. Vascular age, a concept used to assess cardiovascular risk based on arterial structure and function, has been used to predict CVD risk for over a decade
[29]. Methods such as the Framingham 10-year Risk Estimation, published in
Circulation in 2008, utilize parameters such as age, sex, cholesterol levels (or body mass index (BMI)), blood pressure, diabetes history, and smoking history to estimate the vascular age relative to the chronological age
[30]. This tool aids in CVD prevention, early intervention, and management but poses challenges. Vascular age estimation requires not only the acquisition and analysis of population vascular health data at all scales, multiple levels, and high throughput but also an exhaustive understanding of the relationship between hemodynamics, vascular microstructural and mechanical properties, and biochemical information. This demands a deep integration of medicine, physics, biochemistry, and bioinformatics.
Scientific research in vascular EngMed encompasses diverse areas: ① Acquisition and analysis of multiscale, multi-physical field vascular information by developing new functional materials and devices. This approach integrates molecular engineering with multimodal molecular imaging and highly sensitive biomolecular detection. ② Analysis and modeling of vascular information by integrating physiological and molecular biological information into vascular images through theoretical modeling and numerical calculations. ③ Technology for the clinical application of vascular information involves developing clinical decision support systems for cardiovascular and cerebrovascular diseases through extensive clinical sample data storage and deep mining. This process is coupled with model validation and optimization of pathological information. For instance, Fe-based nanomaterials for enhanced magnetic resonance angiography, such as ferumoxytol-enhanced magnetic resonance angiography (Fe-MRA), can facilitate comprehensive vascular imaging (
Fig. 3). Fe-based nanomaterials offer advantages such as a short plasma half-life and lower doses compared to similar products. The current standard Fe-MRA dosage in foreign countries is 4 mg·kg
−1 body weight. In comparison, magnetic nano-iron oxide (Rexon®) requires 3 mg·kg
−1 body weight, ensuring enhanced safety. Fe-MRA exhibits high consistency with digital subtraction angiography (DSA), the gold standard for angiography, and is currently undergoing clinical trials. This research holds a significant societal impact by aiding the early prediction of cardiovascular and cerebrovascular diseases, potentially reducing premature deaths.
3.2.2. Osteo EngMed
Bone tissue engineering is at the forefront of medical innovation, opening new avenues in material research, processing techniques, and bone-healing strategies. Bone defects present a pervasive clinical challenge characterized by surgical complexity, extended treatment duration, and numerous associated complications. Clinicians face significant hurdles in promoting swift and effective bone-defect repair. Current treatment modalities primarily involve surgical interventions, with bone grafting and implantable materials emerging as crucial strategies in contemporary clinical practice to address bone defects and promote repair
[31]. However, some disadvantages, including donor morbidity, risk of immune rejection, disease transfection, and a high rate of failure, limit the clinical application of auto/allografts. In contrast, tissue-engineered bone grafts have shown great potential in repairing bone defects with the benefits of various materials and feasible fabrication processes
[32]. Despite extensive research in orthopedic tissue engineering, there are still no effective treatment options for restoring native tissue structure and function. The complexity of bone composition, organization, and mechanical behavior makes finding suitable biomaterials to achieve optimal regeneration outcomes challenging
[33].
3D bioprinting technology has attracted tremendous attention and has become essential for advancing osteoengineering medicine. It enhances preoperative diagnosis and surgical planning for bone-defect repair by employing computer-aided design to create personalized, 3D models that simulate identical size and shape requirements, thereby improving surgical precision
[34]. For example, Xue et al.
[35] used a 3D model and 3D printing technology for preoperative precision pre-bending for better vascularized fibular grafting to repair and rebuild mandibular defects. They recovered a 3D model of the patient’s defect and used 3D printing to produce a model for the design and preparation of the transplanted fibula. This approach enables surgeons to replicate simulated surgical outcomes during actual procedures, optimizing surgical outcomes and reducing operative time.
In addition to 3D printing, the development of medical materials for bone-tissue repair and regeneration is critical in osteoengineering medicine. Engineering methods that construct the desired micro/nanostructures on the surfaces of medical materials have proven effective in stimulating biological processes essential for bone tissue repair and new bone formation. Wang et al.
[36] reported a “scaffold engineering” strategy in which highly active monoatomic iron catalysts were integrated into 3D-printed bioactive glass scaffolds for the treatment of osteosarcoma, with bioactivities of both antimicrobial and bone defect repair. The engineered scaffolds significantly facilitated osteoconductivity and osteoinduction, which could contribute to the treatment of osteosarcoma and repair of bone defects
[36]. Thus, it is evident that the rapidly growing interdisciplinary collaborations in the fields of materials science and clinical medicine are essential for the successful synergistic solution to clinical problems in the diagnosis and treatment of orthopedic diseases.
3.3. Bioengineering medicine
3.3.1. Cell EngMed
As fundamental units of life, cells possess highly precise and complex systems, offering a new frontier in medical technology through engineering. Cell EngMed employs theories and methods from cell and molecular biology to perform genetic manipulation and large-scale cellular culture with applications in clinical disease diagnosis, treatment, and drug development.
The most advanced and extensively studied technology in cellular EngMed is immune cell engineering for cancer therapy. Chimeric antigen receptor T-cell (CAR-T) therapy exemplifies this approach by genetically modifying human T cells to precisely target and eliminate tumor cells
[38]. CAR-T therapy has proven that robust cellular immune responses can be effectively redirected through engineering
[39],
[40]. Current research focuses on enhancing the specificity, safety, and durability of CAR-T cell therapy, which is essential for clinical translation. For instance, modular CAR-T (modCAR-T) introduces adaptors that enhance antigen recognition specificity and regulate T-cell activation states, achieving a dual effect that potentially reduces tumor evasion
[41]. Clinical trials investigating various drugs are currently underway.
Additionally, stem cell research plays a pivotal role in cell EngMed, showing significant potential in cell therapy, disease modeling, drug development, and the creation of artificial organs
[42]. Advances in gene editing, 3D printing, and biomaterials have led to breakthroughs in stem cell research. However, in China, the clinical application and industrialization of stem cells are still in their early stages, and challenges such as ethical debates, inconsistent standards, and the gap between basic research and clinical implementation, are still present
[43]. Stem cell research also depends on the deep integration and technological breakthroughs of multi-disciplinary technologies, such as medicine, life science, engineering, and materials science, to achieve the goals of tissue repair and organ regeneration in human diseases.
3.3.2. Microbial EngMed
Microorganisms, including bacteria and fungi, form symbiotic microecosystems within the human body and are closely related to human health. The use of microbial EngMed for disease diagnosis and treatment has recently become a popular research topic. Microbial EngMed uses synthetic biology technology to modify microorganisms through genetic engineering, giving them new specific functions that can be used in tumor diagnosis, drug delivery, and wound repair
[44],
[45].
Bacteria were first discovered to induce tumor regression in the 19th century. Recently, microbial EngMed has been used in synthetic biology to improve the efficacy and safety of bacterial cancer therapy. For example, attenuated
Salmonella typhimurium (
S.
typhimurium) VNP20009 was constructed by simultaneous mutation of
msbB and purine biosynthesis-related
PurI genes. This strain showed high safety and could target tumors and inhibit tumor growth in mice
[46]; however, a phase I clinical trial did not show tumor inhibition
[47]. In recent years, researchers have used various methods to enhance tumor colonization and the therapeutic effects of VNP20009, including displaying antigens on bacterial membranes to increase adhesion, designing genetic circuits responsive to the tumor environment, and secreting antitumor factors. Beyond tumor treatment, Cooper et al.
[48] modified
Acinetobacter burnetii to recognize mutant tumor
KRAS DNA for detecting gastrointestinal carcinogenesis (
Fig. 4).
Microbial EngMed has shown promising applications in treating metabolic diseases. For instance, phenylketonuria (PKU), an inherited metabolic disorder, prevents the conversion of dietary phenylalanine (Phe) to tyrosine due to mutations in the
PAH gene, leading to severe health issues such as epilepsy and intellectual disability. Current treatment involves lifelong dietary restrictions. By integrating genes encoding Phe ammonia-lyase and
L-amino acid deaminase into the genome of
Escherichia coli Nissle 1917, researchers at Synlogic created a strain capable of consuming Phe in the human gastrointestinal tract
[49],
[50].
Chronic wound hypoxia can impair cellular repair processes, leading to tissue necrosis, bacterial infection, reduced angiogenesis, and delayed wound healing
[51]. Algae can consume carbon dioxide and water through photosynthesis to produce oxygen. Thus, Chen et al.
[52] used cyanobacteria to create skin patches to provide oxygen to diabetic wounds. Furthermore, hydrogen production by
Chlorella and viable
Bacillus spp. alleviates chronic wound inflammation. Living
Bacillus deplete oxygen in calcium alginate hydrogel beads by respiration, thus allowing
Chlorella to produce hydrogen gas rapidly through photosynthesis
[53]. This wound adjuvant for real-time hydrogen production solves the current problems of low hydrogen solubility and short lifespan in the process of hydrogen transportation in wounds.
3.4. Data EngMed
Data EngMed is an interdisciplinary field rooted in modern medicine and life-science theories. It integrates advanced AI and related engineering technologies such as big data, cloud computing, and machine learning (ML). By harnessing human life and disease characteristics, it seeks to develop intelligent diagnoses, treatment methods, and clinical applications. In recent years, there has been a significant expansion in applying technologies such as the Internet, big data, wearable devices, and AI within healthcare. These innovations empower medical professionals with robust tools and new insights for diagnosing, treating, and monitoring clinical diseases
[54],
[55],
[56]. Since 2016, the Ministry of Science and Technology of the People’s Republic of China, along with the National Health Commission of the People’s Republic of China, and other relevant departments, has issued policies to support the rapid development of “Internet + intelligent healthcare.” This signals a transition from a traditional hospital-centered medical model to a patient-centered, information-linked, and data-driven medical model
[57].
3.4.1. Medical big data
In the age of big data, medical activities, such as medical treatment, medical research, and health management, are constantly generating large amounts of medical data, including electronic health records, clinical treatment, medical images, and multi-omics. These data span micro to molecular and genetic levels, providing extensive research and clinical application value and serving as a foundational resource for medical advancement.
Big data analysis has wide applications in various fields, such as large-scale genetic research, public health, personalized precision medicine, and new drug development. Genomics has witnessed remarkable advancements by leveraging gene sequencing and information technology to build extensive databases
[58]. For instance, the Surveillance, Epidemiology, and End Results (SEER) clinical database, supported by the National Cancer Institute (NCI), aggregates cancer incidence, prevalence, and survival data from US cancer registries. Covering approximately 30% of the US population, this database is a robust resource for researchers to delve into demographic characteristics, population cancer distribution patterns, and the impact of geographical factors on cancer. These insights are pivotal for advancing cancer research and clinical practices
[59]. In China, the integration of big data into medical practice emphasizes auxiliary diagnosis, patient virtual assistants, and medical image analysis, whereas pharmaceutical development lags behind. Given the scale and complexity of big data integration, its effective application requires multi-disciplinary collaboration, incorporating technologies such as large-scale data storage, computing, biostatistics, mathematical modeling, information security, database management, and data mining
[60]. Clinician involvement is essential to effectively harness the potential of big data in medical applications.
3.4.2. AI data engineering
AI technology is accelerating its integration into traditional biomedicine, healthcare, drug development, and other industries. Traditional BME is relatively more concerned with data collection and analysis of single devices, whereas data EngMed is more involved in data fusion and is model-driven from the molecular to individual and group levels, providing more clinical decision support for doctors
[57]. Pharmacy, imaging, pathology, nursing, and public health management can be integrated with AI, and its applications include intelligent drug development, auxiliary diagnosis and treatment, speech recognition and semantic understanding, health management, and hospital management
[61],
[62],
[63].
Accurate prediction of the 3D structure of proteins is crucial for understanding their functions in health and disease. Therefore, the development of methods to achieve this is of great significance for the discovery of new drugs. The most representative tool for predicting the 3D structure of proteins is AlphaFold, which can accurately predict the 3D structure using only AI network methods based on the amino acid sequence of the proteins (
Fig. 5)
[64],
[65]. This breakthrough promises to advance basic research and drug discovery, enabling more effective treatments and preventive strategies. Future AI-driven tools for protein structure prediction are expected to streamline early-stage drug development, optimize preclinical trials, facilitate drug repurposing, and potentially reduce time and cost while benefiting patients.
The development and application of AI technology in medical imaging has also attracted much attention. About 80% of clinical data are stored as images, and most examinations and diagnoses of diseases require reference to these medical images. Medical image processing based on AI deep learning (DL) models can recognize medical images by virtue of perception and cognitive performance, mine important information, and provide help for inexperienced radiologists. Additionally, ML can integrate large amounts of image data and clinical information and train AI systems, making them capable of diagnosing diseases. This is conducive to reducing the missed diagnosis rate and providing effective interaction methods for the deep integration of disease auxiliary diagnosis and large-scale screening systems
[66],
[67].
DL, notably through convolutional neural networks (CNNs) and other sophisticated algorithms, has revolutionized medical image analysis. These algorithms perform rapid and precise object detection, segmentation, tracking, and classification of anatomical structures, thus bolstering clinical decision-making and workflow efficiency
[68],
[69]. Many studies have reported on the application of DL in medical imaging. For example, early pulmonary malignant tumors and precancerous lesions are difficult to identify through traditional diagnostic operations, such as medical imaging, resulting in the loss of the best opportunity for surgical treatment. Researchers have proposed a new hybrid intelligent diagnostic framework, a reliable network based on deep fusion features, which is applied for rapid and accurate detection and classification of malignant and benign tumor cells in lung computed tomography images
[57].
The integration of big data, AI, and other information technologies with medicine through informationized medical and medical research data enables effective and accurate clinical diagnosis, accurate prediction of treatment efficacy, improvement of medical technology and medical service efficiency, and effectively empowers doctors and enhances medical equipment to better serve patients.
Table 1 [37],
[70],
[71],
[72],
[73],
[74],
[75],
[76] summarizes some translational achievements according to the connotations of EngMed.
4. Education in EngMed
4.1. Discipline education of EngMed
EngMed is an interdisciplinary field that integrates digital and engineering technologies into medical theory and practice. Its primary aim is to seamlessly merge foundational medical knowledge with clinical applications, thereby enhancing healthcare quality and efficiency while fostering innovation in medical practice. Although the concept of EngMed is relatively new, leading medical institutions have long implemented advanced educational practices in this domain. Therefore, it is crucial to draw insights from these established practices and experiences by conducting thorough analysis and refinement. The main research directions in EngMed (
Fig. 6) include:
(1) Medical information and intelligent technology. EngMed leverages intelligent technologies to explore medical information and provide foundational insights for clinical diagnosis. Sub-disciplines encompass optical and ultrasound imaging, image processing, disease biomarker identification, development of risk prediction models, queue research, bioinformatics algorithms, and AI models.
(2) Major disease diagnosis and treatment engineering. Developing integrated technologies for the diagnosis and treatment of major diseases is another crucial aspect of EngMed. This involves specialized branches such as vascular health and information engineering, bone network and remodeling, tumor diagnosis, and treatment engineering.
(3) Multiple disease associations and cellular diagnosis and treatment. Constructing models for various associated diseases and developing therapies involving cells and microorganisms are pivotal aspects of EngMed. Branch disciplines include multidisease association models, stem cells, adoptive cell therapies, and treatments involving bacteria and viruses.
(4) Medical AI. It is crucial to utilize digitized medical data, research data, patient profiles, and Big Data to reveal potential relationships and patterns. This supports clinicians in making accurate clinical diagnoses, predicting treatment costs and efficacy, and integrating genetic data from patients for personalized treatment. Analyzing population health data also enables the prediction of disease outbreaks and effective cost-reduction strategies. The key disciplines encompass governance of medical big data, universal medical models, intelligent medical robots, intelligent medical management, and intelligent drug research and development. These areas span digital medicine and the convergence of medical and industrial domains, promising to revolutionize healthcare through interdisciplinary collaboration and innovation.
4.2. Graduate education
To advance EngMed, it is crucial to effectively cultivate talent and organize teams. Education in EngMed-related disciplines should adhere to the principle of “embracing modernization, globalization, and future-oriented development,” with a primary goal of “Training talents for the nation.” The objective is to nurture clinical scientists, clinical engineers, and medical physicists; cultivate versatile high-end professionals who can harness engineering technologies; engage deeply in clinical medicine; and seamlessly integrate medical and engineering disciplines.
EngMed’s education strategy should prioritize cultivating talent with an innovative and critical mindset. They should grasp the fundamental theories of EngMed and related disciplines and be able to conduct independent research. To address practical challenges in clinical medicine and disease prevention, they should explore disease mechanisms, pioneer new diagnostic and therapeutic principles, develop innovative materials, formulations, or instruments, and advance novel clinical application technologies through interdisciplinary collaboration.
EngMed promotes interdisciplinary cooperation, particularly in the integration of natural sciences, engineering (including social sciences such as psychology), and clinical medicine. Focused research within medical institutions explores the organizational and management mechanisms of relevant personnel. Encouraging scientific researchers to collaborate closely with clinical professionals will elevate the standards of diagnosis and treatment, emphasizing practical clinical problem-solving.
Significant emphasis should be placed on professional education in EngMed by optimizing existing medical education models, enhancing interdisciplinary integration between science and engineering, fostering research capabilities, and innovating new teaching and training systems. Employing methodologies such as “learning by doing” aims to cultivate and nurture a cadre of exemplary young talents in EngMed from the early stages of their careers.
5. Differences between EngMed and BME
Understanding the relationship between BME and EngMed requires recognizing their similarities and distinctions. BME integrates physics, chemistry, mathematics, computational science, and engineering to study life, from molecules to organs. It generates knowledge and develops innovative biologics, materials, processes, implants, devices, and informatics methods. BME contributes to disease prevention, diagnosis, treatment, rehabilitation, and health improvement. BME and EngMed are interdisciplinary fields that overlap in various areas
[13],
[70], yet each possesses distinct focuses and characteristics in research, development, and personnel training. The key differences are as follows (
Fig. 7):
(1) Focus and scope. BME aims to innovate materials, preparations, devices, instruments, and informatics methods through comprehensive research and development squarely positioned within the science and engineering realms. EngMed, however, collaborates closely with clinical experts to advance disease research. It integrates existing technologies to develop novel materials, devices, and innovative diagnostic and treatment methods, thereby directly addressing practical challenges in prevention and clinical medicine. This application-centric approach significantly enhances medical diagnosis and treatment in the medical domain.
(2) Research objectives. BME primarily investigates materials, preparations, devices, and systems for humans and other living organisms, along with informatics methods, focusing on interfaces with living systems. In contrast, EngMed primarily focuses on human biology and diseases, adhering to life and medical research paradigms. It integrates advanced technological achievements to solve practical problems and develops relevant clinical applications based on these advancements, continually innovating applicable materials and technologies.
(3) Practitioner roles. BME professionals typically engage in research and development, production, management, and related sectors, requiring specific educational backgrounds, knowledge, and skills. Conversely, EngMed professionals work primarily in medical institutions and related research and development units. They require interdisciplinary knowledge and skills derived from medical education, particularly the ability to apply emerging technologies comprehensively to address human health and disease challenges.
Understanding these distinctions facilitates a clearer perspective on how both fields uniquely contribute to advancing healthcare through innovation, research, and clinical applications.
6. Conclusions
It is encouraging to reflect on how the field of EngMed has grown and matured in recent years. Many dedicated individuals have contributed to this, and EngMed’s innovations continue. The field of EngMed has covered much ground but remains an emerging area of research. It is crucial to encourage interdisciplinary collaboration, particularly between science, engineering (including social sciences, such as psychology), and clinical medicine, to foster further development of EngMed. This integration is essential for driving discoveries and innovations that benefit patient care and medical practices. Furthermore, it is recommended that researchers prioritize practical clinical challenges. Effective collaboration with clinical professionals will enhance the quality and efficacy of diagnostic and treatment methods. EngMed can make meaningful strides toward improving healthcare outcomes by aligning research goals with real-world clinical needs. We hope it will continue to attract attention and receive robust support, enabling it to realize its full potential in revolutionizing medical research and practice.
Acknowledgments
This work was supported by the National Natural Science Innovative Research Group Project (61821002) and the Frontier Fundamental Research Program of Jiangsu Province for Leading Technology (BK20222002).
Compliance with ethics guidelines
Jinhui Wu and Ning Gu declare that they have no conflict of interest or financial conflicts to disclose.
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