Top 10 Frontier Innovations to Watch in 2023: A Comprehensive Report on Emerging Technologies and Their Applications

Over the past year, Orange Bureau has interviewed nearly 100 scientists, as well as dozens of entrepreneurs and investors. In our conversations with them, we have repeatedly discussed the same question: Where will future innovation come from? Through these exchanges and our observations of the industry, we are firmly convinced that future innovation will be technology-led.

Which technologies will define the future? Is it quantum computing, with its strong science-fiction allure; brain science, a key focus of national strategic planning across countries; or spatial omics, hailed by Nature as the next major frontier in “life sciences”?

As we bid farewell to the old and welcome the new, it is the peak season for the release of various rankings. As a vertical media outlet focused on frontier innovation and the commercialization of achievements, VCBeat’s Chengguo Bureau has compiled a list of technological conjectures about future innovation, based on extensive interviews, research, and data collection. Shall we take a look?

The 2022 Nobel Prize in Physics was awarded to scientists Alain Aspect, John Clauser, and Anton Zeilinger, who laid the foundations for quantum computing. They received physics’ highest honor for their detailed characterization of quantum entanglement. These three scientists bridged the gap between theory and practical application, paving the way for today’s quantum computing industry.

In essence, quantum computing is a novel computational paradigm that adheres to the principles of quantum mechanics. In contrast to classical general-purpose computers, its theoretical model is a universal Turing machine reinterpreted through the lens of quantum mechanical laws. From the perspective of computability, quantum computers can only solve problems that are solvable by classical computers; however, in terms of computational efficiency, certain known quantum algorithms outperform classical general-purpose computers in solving specific problems, owing to the principle of quantum superposition.

Academician Pan Jianwei has pointed out that quantum computing is a novel computational paradigm based on quantum mechanics, possessing inherently powerful parallel processing capabilities far surpassing those of classical computing. It offers solutions to large-scale computational challenges required in artificial intelligence, cryptanalysis, weather forecasting, resource exploration, and drug design, while also elucidating complex physical mechanisms such as quantum phase transitions, high-temperature superconductivity, and the quantum Hall effect.

In 2019, Google announced that it had achieved “quantum supremacy,” a milestone featured on the cover of Nature. This technology is poised to significantly impact the healthcare sector, elevating medical decision-making to an entirely new level.

Quantum Computing in Healthcare: Applications Span Drug Design, Clinical Diagnostics, Bioinformatics, and Medical Big DataQuantum computing is transforming healthcare and innovation by enabling faster decision-making, more secure data protection, and cost-effective, high-efficiency data analysis.

In the context of current drug development processes, quantum computing demonstrates potential and value in reducing costs and enhancing efficiency during drug discovery. A drug target refers to the site where a drug binds to biological macromolecules after entering the human body. Target types include biological macromolecules such as receptors, enzymes, and ion channels, serving as the starting point for drug development.

Target identification primarily follows two approaches: one involves crystal structure analysis of macromolecules with abundant experimental data; the other entails predicting molecular structures prior to experimental validation. Regardless of the approach, molecular structure prediction remains indispensable. Both Computer-Aided Drug Design (CADD) and AI-Driven Drug Discovery (AIDD) struggle to achieve dynamic and precise structural predictions without adequate computational power and data support.

The advent of quantum computing holds promise for accelerating target identification. For instance, in protein structure prediction, quantum computing can achieve accurate target identification by simulating amino acids and intermolecular interactions. Many startups, such as D-Wave and ProteinQure, are actively exploring this field.

Drug screening is an essential pathway for discovering lead compounds, predicting the potential efficacy and value of compounds through the evaluation of their biological activity, pharmacology, and toxicology. Currently, there are two primary approaches to drug screening: high-throughput experimental screening and computer model-based virtual screening.

Virtual screening represents a key trend in drug discovery; however, its current value remains limited for compounds lacking experimental data and for high-precision algorithms. Quantum computing, with its advantages in both accuracy and speed, holds significant promise for drug screening. Several pharmaceutical companies have already begun exploring this avenue. For instance, Roche partnered with Cambridge Quantum Computing in 2021 to investigate the potential of quantum computing in compound screening, aiming to leverage this technology to renew efforts against Alzheimer’s disease.

The identification of candidate compounds does not guarantee their efficacy; to varying degrees, these compounds exhibit deficiencies, such as significant toxic side effects and low potency. Therefore, candidate compounds identified through rigorous screening require further optimization to yield molecules with viable drug development potential.

Optimization of compound structures first requires a thorough understanding of the compounds’ structures, followed by predictive assessment of the properties of the modified compounds. Undoubtedly, this may well be a primary domain for quantum computing, as structural adjustments of compounds adhere to the principles of quantum mechanics. In this regard, Google’s quantum computing team has already conducted exploratory research; in 2020, Google’s quantum computer featured on the cover of *Science*, with its key achievement being the simulation of the isomerization reaction of diazene using 12 qubits.

In clinical diagnosis, quantum computing also holds significant application potential.

One aspect is the integration of quantum computing with imaging technology. Magnetic Resonance Fingerprinting (MRF) is a novel technique based on quantum computing that can simultaneously quantify multiple tissue properties within a shorter scan time. In 2018, Microsoft collaborated with researchers at Case Western Reserve University to develop an MRF technology. The researchers applied advanced quantum computing to create higher-quality imaging techniques and utilized the HoloLens augmented reality platform to display 3D images to physicians. Based on this platform, doctors were able to assess therapeutic efficacy in cancer patients after a single session of chemotherapy.

Integrating quantum computing with machine learning for medical diagnosis and probabilistic reasoning represents another promising direction. Scientists at Cambridge Quantum Computing (CQC) have developed a method demonstrating that quantum machines can infer hidden information from highly general probabilistic reasoning models. These approaches can enhance numerous applications, including reasoning in complex systems and quantifying uncertainty, such as in medical diagnosis, fault detection in mission-critical machinery, or financial forecasting for investment management.

Furthermore, quantum computing can more easily organize complex, high-dimensional data, making it suitable for the intelligent enhancement of diagnostic techniques such as magnetic resonance imaging and angiography.

Since computation is involved, quantum computing technology and bioinformatics are bound to have compelling stories to tell.

Quantum computing can efficiently generate complex data distributions and play a significant role in processes such as gene sequence alignment, comparison, and assembly. By leveraging approximate optimization algorithms, it reduces computational workload and shortens processing time. Furthermore, in the integration of multi-omics data, quantum computing can identify patterns and regularities within complex data distributions more rapidly and accurately than classical computational methods. This advancement presents new opportunities for the application of medical big data.

Beyond data storage and structuring, big data applications also involve computation. With the advancement of multi-omics, medical big data can no longer be adequately described merely as “massive,” and the associations between data and clinical phenotypes have become increasingly complex and diverse. Existing computational capabilities are inadequate to meet these demands, necessitating computational methods that offer greater processing power, faster computing speeds, and more advanced deep data processing capabilities. Quantum computing may well be the powerful tool that enables researchers to extract insights from vast amounts of unstructured data.

Building on the previous discussion, quantum computing can identify patterns within massive datasets or structure unstructured data. What, then, can be done with such data? One answer is readily apparent: personalized medicine or health management.

The author recalls Human Longevity, a U.S. healthcare company originally founded by Craig Venter. Setting aside the complex relationship between the company and its founder, let us examine his business logic:

Initially, Human Longevity aimed to leverage emerging technologies such as genomics, stem cell technology, big data, and artificial intelligence to identify the root causes of human aging and the underlying mechanisms of age-related diseases, with the goal of discovering corresponding drugs or therapies to slow down the aging clock. However, after a challenging development journey, this ideal has remained elusive. What has truly enabled Human Longevity to achieve industry leadership is its premium health screening and health management services, built upon clinical diagnostic technologies such as gene sequencing.

Two aspects of Human Longevity are worth contemplating: first, that multi-omics-based health management represents a viable business model; and second, whether the business logic for drug or therapy development based on multi-omics technologies can be enhanced by quantum computing.

In other words, the computational power of quantum computing offers new pathways for healthcare and drug development. If massive amounts of medical data can be leveraged through quantum computing, it may bring transformative experiences to clinical diagnosis and treatment, health management, and pharmaceutical R&D. It is precisely for this reason that, despite some applications remaining in the realm of speculation, countless researchers, tech giants, and startups are actively pursuing this frontier.

China has become the only country in the world to achieve the milestone of “quantum computational advantage” in two distinct physical systems. In terms of fundamental research, China’s quantum physics research ranks among the world’s leaders, with two achievements selected as one of the “Top Ten” advances in international physics. On the industrial front, both quantum computing and its industrial applications have already commenced in China, attracting joint attention from capital markets and industry players. Although specific applications lag slightly behind those in the United States, underpinned by strong foundational research and robust market demand, China’s quantum computing sector may hold limitless potential.

Organs can regenerate, and humans can achieve immortality... Regenerative medicine is turning scenarios once confined to science fiction into reality. Regenerative medicine refers to the application of biological and engineering principles to create tissues and organs that have been lost or functionally impaired, promoting the body’s self-repair and regeneration so that they regain the structure and function of normal tissues and organs. Beyond skin and tissue regeneration, which are already being gradually realized, the emergence of new materials and technologies is making more ambitious “regenerative” feats possible.

Bioregenerative materials possess excellent tissue-inductive properties. When implanted into the human body, they can induce the regenerative repair of defective tissues and organs, with applications in orthopedics, neurosurgery, cardiovascular medicine, ophthalmology, stomatology, medical aesthetics, and other fields. In regenerative research, these materials primarily serve two functions: first, to promote regeneration, and second, to provide structural support for cell growth.

Currently, bioregenerative materials/products that have successfully entered the market are primarily targeted at skin and bone tissue regeneration, which involve relatively simple compositions and structures. Emerging cross-disciplinary integrations may enable the “regeneration” of more complex tissues. For instance, tissues incapable of self-regeneration, such as organs or missing tissues, can be regenerated by combining regenerative materials with stem cells for in vitro cultivation.

Molly Stevens, Professor of Biomaterials Science at Imperial College London, mentioned in an interview that she has conducted numerous stem cell experiments worldwide using various types of cells. However, the results have consistently been the same: these cells typically die after transplantation. Nevertheless, if combined with biomaterials to form an in vivo bioreactor, their survival rate would undoubtedly be significantly improved. Professor Huiqi Xie’s research group at Sichuan University has investigated mesenchymal stem cells from diverse sources. Building on urine-derived stem cells, the team has carried out extensive research on the repair of kidneys, urethras, bladders, cardiac muscle, and esophagus, as well as key repairs involving stem cells and cartilage.

Regenerative medicine research based on material-stacked stem cells is emerging as a future trend in the field. Guided by this approach, the realization of organ regeneration may not be far off.

In addition to trauma, aging also inflicts damage on cells, organs, and tissues. This process is chronic and irreversible. Upon entering cellular senescence, cells exhibit an irreversible, apoptosis-resistant state of mitotic arrest, leading to functional alterations in gene expression, chromatin structure, and cellular behavior. At the organismal level, this manifests as declined organ function and the onset of certain diseases.

Humanity has long been obsessed with finding the secret to immortality, only to discover that as life expectancy increases, aging itself becomes a risk to health. Today, population aging poses a significant threat to global economic growth and sustainable development. Nearly a century has passed since scientists first demonstrated that the aging process is malleable, sparking a proliferation of research avenues—including caloric restriction, senolytic therapies, stem cell treatments, and microbiome interventions. Although the challenge of an aging population continues to intensify, deeper insights into the biology of aging are bringing the goal of delaying senescence within reach.

It is undeniable that China’s regenerative medicine sector remains in its nascent stages. Products such as bone repair materials, regenerative aesthetic treatments, and dental restorations have already achieved commercialization, whereas high-barrier technologies like regenerated organs are still confined to the research phase. Reportedly, no regenerative kidney or heart products have entered clinical trials globally. Beyond breakthroughs in basic and applied research, industrialization requires coordinated efforts from regulatory bodies, capital markets, and upstream and downstream stakeholders.

Digital technology is a scientific and technological discipline that has emerged alongside electronic computers. It refers to the technology that leverages specific devices to convert various types of information—including graphics, text, audio, and video—into binary digits “0” and “1” recognizable by electronic computers, thereby enabling computation, processing, storage, transmission, dissemination, and reconstruction.

The integration of digital technologies into medical processes has given rise to digital healthcare. The emergence of digital medical devices and digital therapeutics has significantly enriched clinical diagnosis and treatment. Driven by data accumulation from healthcare informatization, the interactive models of internet-based healthcare, and the advancement of emerging technologies such as AI and 3D printing, digital healthcare is fostering a new business paradigm.

The digitization of healthcare begins with the digitization of diagnostic and treatment equipment, which forms the foundation of digital medicine. Digital medical devices refer to those based on computer technology, where signal acquisition and power input are positioned as close to the front end as possible, and all processes—including control, data acquisition, processing, storage, transmission, and execution—are fully digitized. Furthermore, digital medical devices provide clearer images and more precise quantitative measurements, offering the material and technical support necessary for physicians to make more accurate diagnoses. Medical devices operating under computer software have gradually replaced conventional equipment, becoming the mainstream in clinical settings.

The emergence of digital medical devices has significantly enriched the connotation and capacity of medical information. The visualization of electrophysiological data, such as electrocardiography (ECG) and electroencephalography (EEG), along with three-dimensional and four-dimensional visualization of imaging modalities like CT and MRI, demonstrates how the integration of digital technology with healthcare is delivering entirely new experiences to clinical departments.

Visualization is a primary direction for digital medical devices; therefore, digital imaging equipment constitutes an important component of digital medical devices. Most such devices can acquire images directly through digital interfaces. Currently, the interfaces for these devices are undergoing regional standardization, with corresponding regulations and standards already established for both actual and simulated images.

Furthermore, through the integration of multidisciplinary advanced technologies, digital medical devices are often capable of generating deep, high-definition images. This facilitates earlier confirmation of diseases, lesions, and pathological conditions, yielding clearer and more precise results, while significantly enhancing the efficiency and quality of clinical diagnosis and treatment.

Certainly, beyond visualization, the trends toward networking and intelligence are also clearly evident.

For a long time, hospitals have been seeking safe, efficient, and convenient management solutions. Data management has always been a challenge in hospital administration, requiring data quality to maintain accuracy, consistency, and timeliness. The collection and interconnection of underlying data need to be timely, accurate, and highly efficient, while the structuring of data requires deep intelligence... All of these are expected to be achieved through innovative digital hospital management.

Administrators can monitor hospital operations and the performance of various departments in real time via the network, ensuring that the hospital always operates at peak efficiency. Furthermore, the hospital can provide patients with necessary diagnostic and treatment information at any time. Meanwhile, leveraging insights from underlying data, the digital management system can deliver personalized management solutions tailored to the hospital’s specific circumstances. These solutions cover areas such as hospital performance evaluation, physician management, medical record management, and hospital equipment management.

As digital technology becomes deeply integrated into healthcare scenarios, new technologies and payment models have become endogenous drivers for expanding the supply of high-quality medical services. The innovative convergence of “healthcare + pharmaceuticals/medical devices + insurance” is benefiting a growing number of patients. The healthcare payment system is undergoing transformation; in addition to the centralization of payment responsibilities on patients, commercial health insurance, pension insurance, and long-term care insurance are also demonstrating significant potential.

As the insured population expands and data becomes more abundant, several changes may emerge in healthcare payment. First, insurance products may become more differentiated, with tailored offerings for distinct groups (such as patients versus healthy individuals), or differentiated premiums within the same insurance product based on group characteristics. Second, data may drive value creation, such as developing personalized health management solutions based on health databases, or enabling more precise calculation of claims.

Digital solutions are present in all these scenarios. Digital healthcare, on the one hand, addresses challenges inherent in the application of traditional medical systems, and on the other, enhances payment efficiency.

Beyond clinical and consumer applications, digital technologies also hold significant promise in the product domain.

First, in the R&D scenario, just as digitalization manages hospitals and payment systems, digital technology can also empower the research and development of drugs and medical devices, enabling management of the drug development process through digital platforms. Second, by leveraging digital solutions to mine clinical data and conduct bioinformatics analysis, greater efficiency is achieved compared to traditional analytical methods, thereby increasing the likelihood of achieving “drug repurposing” through data mining.

Next is the production phase, where machine vision can replace human inspectors. This not only reduces labor costs throughout the entire production process but also improves the accuracy and efficiency of inspections. Such applications have already been implemented by some companies as pioneers. After multiple rounds of training with simple models and complex features, the consistency rate between machine vision and manual annotation has reached as high as 98.879%.

Finally, in the distribution segment, data still holds substantial value waiting to be unlocked. For instance, demand for medications associated with certain diseases may exhibit significant geographic concentration and temporal periodicity. Consequently, a wide array of AI algorithms have emerged, making drug demand forecasting increasingly feasible. Participants in the pharmaceutical and medical device distribution industry are also leveraging the momentum of digital transformation, utilizing edge-based real-time computing to achieve more accurate predictions, thereby formulating more scientific and rational strategies for drug sales and inventory management.

Leveraging digital technology, a scenario once considered a futuristic vision is gradually becoming a reality: today, patients can download an app for disease treatment based on a physician’s prescription. Apps are emerging as a form of therapy, either used independently or in combination with traditional pharmaceuticals, to deliver more efficient and widely accessible treatment. This is the concept of “Digital Therapeutics,” which has garnered significant attention across the industry.

Digital therapeutics are evidence-based therapeutic or interventional measures provided to patients. Driven by high-quality software programs, these interventions represent the digitalization of healthcare services, with core functions aimed at preventing, managing, or treating specific diseases. They can be used independently or in conjunction with medications, medical devices, or other therapies.

Currently, most digital therapeutics focus on conditions requiring intensive intervention, such as psychiatric disorders, chronic diseases, and mental health conditions. Compared with traditional therapies, digital therapeutics enable remote consultation and treatment, allow for personalized customization based on patients’ temporal and spatial contexts, and are also more cost-effective.

Of course, the value of digital technology extends far beyond this. It is not only transforming clinical practice and product research and development, but also delivering a more comfortable experience to patients and consumers. Individuals can schedule appointments online from the comfort of their homes. Patients no longer need to wait in examination rooms for test results; various diagnostic images and data can be transmitted directly via the internet to attending physicians, enabling timely and accurate diagnosis and treatment. Leveraging private healthcare services and public medical consultation platforms based on the internet and cable television, the system can proactively remind the general public to undergo health screenings, predict the onset and progression of certain diseases, and recommend new treatment options to patients. This allows patients to enjoy personalized physician-led healthcare services without leaving home. While digital healthcare has already reached a significant level of maturity and garnered widespread attention, we are truly just at the beginning, and the future may hold even more surprises.

Brain-Computer Interface, sometimes referred to as "brain port" or "brain-machine fusion perception." Here, "brain" refers to the brain or nervous system of organic life forms, while "machine" denotes any device capable of processing or computation, ranging from simple circuits to silicon chips.

Brain-computer interface (BCI) technology is hailed as the “information superhighway” for communication between the human brain and the external world, and is widely recognized as a key core technology for next-generation human-computer interaction and human-machine hybrid intelligence. Beyond the science-fiction-inspired concept of “mind control,” BCI has more clearly defined applications in the medical field, particularly in motor nerve rehabilitation and the treatment of neurological disorders. Unlike previous exoskeleton-assistive devices, BCI-based neural repair represents a visible, true form of “control restoration.”

Neuromodulation is the process of writing signals to the brain within the closed-loop signal pathway of brain-computer interfaces (BCIs). By delivering stimuli via different types of signals, it can improve and treat certain neurological conditions. Currently, BCIs based on electrical, acoustic, optical, and magnetic neuromodulation have achieved commercialization, ranging from cochlear implants and deep brain stimulation to transcranial magnetic stimulation and functional near-infrared spectroscopy. In the future, these technologies are expected to be expanded to improve or treat other diseases.

Brain-computer interface (BCI) technology enables real-time monitoring of patients’ electroencephalographic (EEG) states. By training and modulating brain signals to influence cortical activity, it facilitates neural exercise, thereby enhancing brain functionality and connectivity. This achieves active-passive collaborative rehabilitation training under the patient’s “mind control.” It overcomes the limitations of traditional rehabilitation methods, which are passive and monotonous, by enabling active rehabilitation driven by the patient’s intent, significantly improving therapeutic outcomes.

By processing neuromuscular signals with artificial intelligence algorithms and combining them with built-in sensors to identify user intent, these systems enable intuitive control (“mind-guided movement”) and seamless switching between walking and running, thereby creating a high quality of life for people with disabilities. Currently, most such products are non-invasive. As technology matures and advances, integrated implantable devices are becoming a reality. These products primarily target severe conditions such as amyotrophic lateral sclerosis (ALS) and high-level paraplegia. With further technological progress and the maturation of regulatory frameworks, invasive brain-computer interface (BCI) products may offer even greater possibilities in the future.

China Brain Project adopts a comprehensive strategic layout of “one main body with two wings,” focusing on the neural basis of brain cognition as the core, while advancing foundational arrangements in the diagnosis and treatment of brain disorders and brain-inspired intelligence technologies. It conducts brain science and brain-inspired research from three perspectives: understanding the brain, protecting the brain, and simulating the brain.

China’s 14th Five-Year Plan outlines five key research areas, incorporating brain-computer integration into the category of priority technologies. In 2021, China officially launched the “Brain Science and Brain-Inspired Research” megaproject under the Science and Technology Innovation 2030 framework, with a budget exceeding RMB 10 billion. Currently, research under the China Brain Project has entered the phase of practical implementation.

Current brain-computer interface (BCI) products are predominantly non-invasive, a trend likely attributable to factors such as technological maturity, regulatory approval processes, and the clinical demand for specific indications. Furthermore, intracranial implantable materials are subject to stringent requirements for biocompatibility and stability; consequently, the advancement of these implantable materials, along with associated chips and components, is closely intertwined with the development of the BCI industry.

Nanotechnology represents a strategic high ground in the 21st century. When any structural unit of a material within its three-dimensional space is reduced to the nanoscale, its properties undergo significant changes. Such materials are referred to as nanomaterials. Because their dimensions approach the coherence length of electrons, their properties change substantially due to self-organization driven by strong quantum coherence. Furthermore, as their size approaches the wavelength of light and they exhibit unique surface effects owing to their large specific surface area, their manifested characteristics—such as melting point, magnetism, optical properties, thermal conductivity, and electrical conductivity—often differ markedly from those observed in their bulk state.

The applications of nanomaterials in healthcare are summarized under the field of nanobiology and medical research. This field encompasses the properties and characterization techniques of relevant materials, as well as research on the detection and regulation of biological processes based on these materials, constituting a significant component of nanoscience. Specifically, the medical applications of nanomaterials can be categorized into the following aspects.

In fact, as early as the 1960s, it was discovered that certain nanomaterials, when used as carriers, could readily cross numerous biological barriers. Consequently, nanomaterials began to be applied in the field of drug delivery. In 1965, when British scientist Bangham discovered liposomes, research into nanoscale drug carriers commenced. The Chinese pharmaceutical community also introduced nano-drug delivery systems to China at an early stage and initiated related research.

Research on nanomaterials in drug delivery also follows multiple pathways, such as using liposomes to carry drugs, employing polymeric micelles, utilizing nanorobots, or enabling self-assembly of drugs through self-organizing drug delivery systems.

Nanomedicines are nano-assemblies created by combining biologically active molecules, such as drugs, with carrier materials using nanobiotechnology. These assemblies leverage nanoscale effects to modify the pharmacokinetics, efficacy, and pharmacological properties of the loaded active ingredients, thereby achieving significant clinical advantages. The encapsulated agents may include small-molecule compounds such as alkylating agents and antimetabolites, as well as macromolecules like peptides, proteins, and nucleic acid therapeutics, or contrast agents.

Initially, the primary roles of nanomaterials in drug delivery were transport and sustained release; nowadays, the focus has shifted more toward targeting and the delivery of macromolecular drugs. However, researchers have already discovered nanoparticles that can both carry drugs and exert therapeutic effects. Consequently, it is difficult to define the future of nanomedicine through the lens of current understanding.

Nanoregenerative Medicine is a term in biophysics published in 2018, referring to the discipline that utilizes nanomaterials and technologies to mimic the microstructures of human or animal tissues or organs, with the aim of studying their replacement or inducing the regeneration, reconstruction, or restoration of normal function of tissues or organs.

Regenerative medicine primarily encompasses cell-based regeneration, material-based regeneration, and research on the combination of cells and materials. Material-based regenerative research is achieved through materials possessing tissue-inductive properties. Cells expanded extensively in vitro are seeded onto porous scaffolds made of tissue-inductive materials and cultured either in vitro or in vivo. Subsequently, the biological matrix degrades after cell growth is complete, ultimately yielding living cells, organs, or organoids. In the field of material-related regenerative research, nanomaterials hold a predominant position.

As the foundation of development, materials are widely used in the medical field, ranging from gauze and syringes to medical equipment and devices, as well as replacement human tissues and organs. The application of materials in medicine covers nearly every aspect.

In the field of medical devices, nanomaterials are ubiquitous, ranging from engineering fundamentals and implantable materials to biosensors and probes. For instance, antibodies can be conjugated to nanoparticles for targeted diagnosis of specific molecules, and imaging agents containing nanoparticles can be prepared or constructed using nanotechnology to enhance contrast in medical imaging. Molecular diagnostics and bioimaging technologies developed based on nanomaterials, along with fluorescence and biochemical detection techniques, show great promise for the early diagnosis of diseases.

Nanomaterials have demonstrated exceptional capabilities across various industries and fields, including biomedicine, energy, and chemical catalysis, garnering significant attention from numerous countries and research institutions worldwide. In 2000, the United States enacted the National Nanotechnology Initiative, prioritizing “nanoscience, nanomaterials, and materials with new production technologies” as key areas for development.

During the Eighth Five-Year Plan period, China also included "nanomaterials science and technology" in its National Climbing Program, and issued the Outline of National Nanotechnology Development in 2001. During the 13th Five-Year Plan period, China established the National Center for Nanoscience and Technology, the National Nanotechnology Industrialization Base, and the National Engineering Research Center for Nanotechnology and Applications to promote basic research, applied research, and industrialization.

There are over 70 nanotechnology research platforms associated with the Chinese Academy of Sciences (CAS) system and various universities. Whether viewed from the perspectives of strategic orientation, industrial demand, or research momentum, nanomaterials are arguably the most noteworthy sector within the field of biomedical materials, perhaps without exception.

The large-scale application of sequencing technology has ushered modern medicine into the era of precision medicine. Or, more accurately, it has done so at the molecular and genetic levels. Driven by genetics, the development of existing diagnostic and therapeutic approaches has adopted entirely new paradigms, bringing once seemingly unattainable treatments—such as cell therapy, gene therapy, and immunotherapy—into reality. Molecular genetics has become widely recognized in recent years; indeed, it is already a practical technology, making its inclusion in discussions of future prospects seem somewhat incongruous. However, this perspective is adopted because the achievements made thus far represent only a brief initial step. Much like our limited understanding of genetic loci, there is still a long journey ahead in the exploration of molecular genetics. Naturally, these future explorations will likely focus more on the single-cell level and the expression of specific genetic loci.

DNA sequencing has continued to dominate over the past decade, with exponentially growing whole-genome maps illustrating regulation at all levels (DNA, RNA, and histones). Extensive research has been conducted by the scientific community on transcriptomes, histone modifications, and transcription factors, accumulating an unprecedented volume of data. These maps are providing deeper insights into the relationship between genes and expression, while enhancing our understanding of the roles and mechanisms of epigenetic regulators, including methyltransferases, reader proteins, and demethylases. Large-scale integrative and multi-layered epigenomic analyses have yielded a relatively comprehensive landscape of the epigenome, including its dynamics in development and disease. Nevertheless, many mysteries remain unresolved, such as the precise functions of cytosine methylation, the distinct mechanisms of methylation in vertebrates versus invertebrates, and the reasons for embryonic lethality in knockout mutants during development.

In most cases, the analysis of the epigenome has evolved into a highly valuable yet still descriptive understanding of many epigenetic layers. However, many questions remain to be answered in the future.

Francis Collins once stated that, with the exception of trauma, human diseases can essentially be described as the result of the interplay between genetic factors (internal causes) and environmental factors (external causes). Whether in cell therapy or immunotherapy, the logic underlying drug and therapeutic regimen development is increasingly returning to the genetic molecules themselves, as well as the gene and protein levels. Many of today’s breakthrough treatments also originate from insights into genetics or disease pathogenesis. As detection tools for genetics, genes, and protein expression become increasingly mature, clinical diagnosis and treatment are poised to undergo a fundamental transformation.

However, as previously mentioned, our understanding of the pathway from DNA to transcription, to proteins, and finally to phenotypes remains limited. Therefore, despite the long-standing research into the relationship between diseases and genes, this will continue to be a central theme in the years to come.

Nucleic acid-based drugs are hailed as the third revolution in the biopharmaceutical industry, with mRNA therapy emerging as the star among nucleic acid-based therapeutics around 2017.

mRNA, or messenger RNA, is a type of single-stranded ribonucleic acid transcribed from one strand of DNA as a template. It carries genetic information that guides protein synthesis and plays a crucial role in the process of protein coding. In simple terms, mRNA is transcribed from DNA and directly directs protein translation, serving as the direct instructional unit for carrying out life activities, whereas proteins are the primary functional units executing these activities. Therefore, based on the central dogma of protein translation, mRNA-related drugs, vaccines, and biological therapies have become hot topics in recent years.

With the advancement of technologies such as mRNA modification and delivery, mRNA-based protein replacement therapy has emerged.

Protein replacement therapy involves the introduction of therapeutic antibodies and functional proteins. By delivering mRNA via injection to transfect somatic cells, the mRNA is translated into proteins that either replace abnormal proteins or supplement deficient ones. This approach has seen initial applications in hereditary metabolic diseases, solid tumors, cardiovascular diseases, and other fields. Compared with other applications, the development of mRNA-based protein replacement therapy remains in its early exploratory stages; however, existing clinical data demonstrate its substantial potential, with preliminary validation of both its safety and efficacy.

Synthetic mRNA can serve as a template for the synthesis of any protein, protein fragment, or peptide, with numerous applications in drug research, including tumor immunology.

mRNA cancer vaccines can be regarded as a segment of synthetically produced mRNA. Following injection, the proteins encoded by the mRNA are synthesized by ribosomes, undergo post-translational modifications to yield correctly folded functional proteins, and are then presented to the immune system. This process mimics the natural mechanism of RNA virus infection and the subsequent induction of protective immune responses. Theoretically, mRNA cancer vaccines can encode any protein and may modulate the tumor immune microenvironment by encoding specific proteins, thereby overcoming tumor immune tolerance. This has become a significant direction in mRNA cancer vaccine research. For instance, BioNTech is combining next-generation CAR-T therapy with mRNA vaccines for research into the treatment of solid tumors.

As mentioned above, synthetically produced mRNA can encode any protein, generating target proteins or immunogens that activate the body’s immune response to combat various pathogens. Furthermore, this class of vaccines offers advantages in terms of safety and production costs. mRNA vaccines utilize the viral genetic sequence rather than the virus itself; they contain no viral components and pose no risk of infection. In terms of manufacturing, mRNA does not require adjuvants, facilitating scalable production at low cost.

There are countless mRNA vaccine companies worldwide, with BioNTech, Moderna, and CureVac being the most prominent. The product pipelines of these companies are largely focused on oncology. However, compared to the 8–14-year development cycle of traditional vaccines, mRNA vaccines have a significantly shorter development timeline. This suggests that, beyond oncology, mRNA vaccines may hold greater potential for application in the prevention of a broader range of diseases and infectious diseases.

Intelligent construction has seen relatively mature applications in traditional industrial sectors. For instance, in industries such as food processing and automobile manufacturing, the implementation of fully automated production lines has partially replaced, and in some cases completely supplanted, manual labor. In recent years, laboratories in the medical and scientific research fields have also recognized the value of intelligent transformation. Examples include replacing highly repetitive manual tasks with automated equipment or assembly lines, leveraging informatization to digitize laboratory data and thereby minimize human recording errors, and utilizing digitalization to enhance operational efficiency. In the medical field, various types of laboratories—including those for clinical testing, drug development, and biological experiments—have begun adopting automation, informatization, and digitalization to achieve multidimensional “intelligence.” Such facilities are referred to as Smart Medical Laboratories.

Structure-Activity Relationship (SAR) of drugs refers to the relationship between the chemical structure and pharmacological efficacy of a drug. Traditional SAR studies primarily focus on qualitative analysis, involving the prediction of the relationship between molecular structure and biological activity, followed by the determination of the target enzyme's active site structure for rational design. In automated settings, Quantitative Structure-Activity Relationship (QSAR) methods, aided by computational tools, have become one of the key approaches in rational drug design.

In the drug design phase, the 3D structure of target proteins is crucial for structure-based drug discovery. Artificial intelligence and automated tools have already shown initial success; for instance, AlphaFold has achieved considerable accuracy in predicting the 3D structures of target proteins. Furthermore, the combination of artificial intelligence and cryo-electron microscopy has enabled a bidirectional improvement in both the efficiency and precision of drug development.

Intelligent sample cultivation, storage, and management enable laboratories to handle samples more efficiently, systematically, and securely. For instance, precise control of temperature and humidity ensures proper sample preservation, preventing sample loss due to human error. Furthermore, end-to-end automated sample preparation, powered by robotics, AI, and automation technologies, not only reduces labor requirements but also ensures that samples prepared under identical conditions consistently meet the same standards and quality levels. This approach supports parallel sample preparation for multiple types of experiments and simultaneous pre-processing of the same sample for various analytical workflows, thereby guaranteeing sample consistency and ensuring the authenticity and reliability of the resulting data.

In the experimental phase, data acquisition from instruments is merely the first step; it typically requires lengthy and meticulous data analysis and processing to yield results for a given stage. Here, “data” may refer not only to quantitative measurements but also to images and textual records documented by researchers. Particularly in research-oriented laboratories, as the dimensionality of data and processing requirements become more complex, substantial computational resources are needed in addition to modular data management. Furthermore, data storage, retrieval, and querying often constitute complex tasks. Intelligent experimental data management enables secure, efficient, and organized storage, significantly enhancing experimental efficiency.

There are many more applications for smart laboratories; indeed, this is an open-ended “topic,” with its application scenarios determined by the laboratory’s need to reduce costs and improve efficiency. Compared with the other technologies discussed in this article, automated laboratories are essentially products built on a combination of multiple technologies. While their underlying technologies may originate from the research sector, their application and commercialization are more often driven by commercial companies.

Spatial omics, listed among Nature’s seven technologies to watch in 2022, is hailed as the next major frontier in the life sciences.

Proteins are functional molecules in all cells and serve as effector products of all biological processes. Spatial protein expression is critical for determining the precise localization and function of proteins within tissues, and it can vary with cell type, cell cycle progression, disease status, and diagnostic and therapeutic interventions. Therefore, spatial proteomics can be used to investigate changes in disease-associated spatial protein expression profiles, offering novel perspectives for identifying biomarkers and developing new diagnostic and therapeutic strategies. In recent years, significant advances have been made in spatial proteomics research in areas such as the microenvironment and disease progression, mechanisms and drug targets, organ structural heterogeneity, and spatial atlases of tissues and organs.

Spatial proteomics opens up entirely new research perspectives in areas such as elucidating tissue microenvironments, developing tissue biomarkers, disease diagnosis and prognosis, and precision medicine, while also holding promise for advancing clinical diagnostics toward a deeper understanding of the fundamental nature of diseases.

One of the breakthroughs of spatial proteomics in clinical diagnosis lies in the detection of the cellular microenvironment. In clinical research, current efforts in spatial proteomics are increasingly focused on applications within the tumor microenvironment.

The cancer microenvironment resembles an ecosystem, comprising a mixture of diverse cell populations and species. For instance, species richness (which can also refer to intratumoral heterogeneity) may be associated with the durability of immunotherapy responses and long-term patient prognosis. Metabolic competition between immune cells and cancer cells, termed interspecific competition, is also a key determinant of cancer progression.

By providing precise spatial coordinates for cell and molecular profiles at the systems level, spatial omics is transforming our understanding of the cancer microenvironment. For instance, in a Nature Biotechnology article titled “Deep Visual Proteomics defines single-cell identity and heterogeneity,” researchers demonstrated that acquiring spatial multi-omics maps enables the holistic reconstruction of key processes in tumorigenesis.

Spatial proteomics technology can distinguish cells with specific phenotypes and analyze their potential functions through proteomics. It precisely identifies characteristic proteins associated with immune regulation and DNA replication that are exclusively present in tumor regions within melanoma tissues. This enables the visualization of dysregulated key pathways involved in cancer progression across two-dimensional tissue sections.

Spatial proteomics enables the direct comparison and contrast of individual cell types and states within their native tissue environments, yielding protein profiles linked to specific cell types and spatial contexts. This approach facilitates the identification of more precise key therapeutic target proteins and diagnostic/therapeutic biomarkers for diseases. For instance, the article titled “Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts,” published in Nature, conducted spatial proteomic analysis of ovarian cancer and its adjacent stroma. This study successfully identified NNMT as a key regulatory target through which stromal cells influence tumor metastasis, thereby providing new insights for future targeted therapies against ovarian cancer metastasis.

Some researchers have also successfully applied spatial proteomics to study diseases, including acute viral infections and liver diseases, or to elucidate the cellular defects underlying monogenic disorders. Spatial proteomics has now reached a stage where it can be integrated with other “omics” technologies, cell biology, and medical research, thereby providing unbiased, systems-level insights into biological processes. Currently available spatial proteomics methods are highly complementary, with their respective developmental advantages and limitations making them suitable for different types of applications.

Since Antonie van Leeuwenhoek invented the first microscope, humans have begun exploring and observing the microscopic world. In the history of research into the microscopic realm, the optical microscope has made indispensable contributions. Based on optical microscopy, people observed cells for the first time and discovered microorganisms.

However, as microscopic research has progressed from the micro-world to microstructures, the limitations of optical microscopy have become apparent. Constrained by the optical diffraction limit, optical microscopes cannot observe molecules and structures beyond this threshold. Although microscopes with nanoscale resolution, such as scanning electron microscopes, have subsequently been developed, they still exhibit certain drawbacks in practical applications.

2006 marked the advent of super-resolution fluorescence microscopy techniques. American scientist Eric Betzig and colleagues first proposed the concept of Photoactivated Localization Microscopy (PALM) in the journal Science, enabling humans to optically visualize the nanoscale microscopic world with precision beyond the diffraction limit. He was subsequently awarded the 2014 Nobel Prize in Chemistry.

Also in 2006, the team led by Xiaowei Zhuang at Harvard University successfully developed Stochastic Optical Reconstruction Microscopy (STORM). By eliminating the photobleaching step, STORM offers advantages over PALM in rapid data acquisition.

Unlike other super-resolution observation methods, it enables live-cell imaging. Therefore, the application of super-resolution imaging technology is mainly concentrated in the fields of biology, agronomy, animal husbandry, and veterinary science.

When observational resolution can reach the subcellular level, biological research may be redefined.

For instance, in osteocyte research, fracture repair is associated with osteoblasts and osteoclasts. Both osteoblasts and osteoclasts found in bone tissue are involved in fracture healing. The primary distinction between them lies in their functions: osteoblasts are responsible for bone formation and mineralization, whereas osteoclasts mediate bone degradation and resorption. By integrating gene editing with super-resolution imaging techniques, researchers can observe and investigate how the knockout of different genetic loci affects the relationship between protein expression and cellular phenotypes. This approach holds promise for identifying expression mechanisms and therapeutic strategies that facilitate bone repair.

Moreover, the visualization of subcellular structural details signifies a breakthrough in tools for studying protein localization and function, enabling the direct observation of the dynamic characteristics of these target molecules at the single-molecule level. Take neuroscience as an example. The brain is a complex network of neurons; the human brain contains over 80 billion neurons, each connected by thousands of synapses. Due to the size constraints imposed by the diffraction limit, fluorescence microscopy techniques cannot observe phenomena such as signal transmission at neural synapses. Super-resolution imaging technology, however, makes this possible. By using imaging techniques to observe neurotransmitter transport and release between neural synapses, as well as protein folding, our understanding of the nervous system and neurological disorders may undergo a qualitative leap.

Although optical and electron microscopes can visualize various organelles, including mitochondria and chloroplasts, they are inadequate for elucidating how these organelles function and operate. Breakthroughs in super-resolution imaging technology have facilitated significant advances in the study of organelle function and dynamics, such as intracellular lipid storage and metabolism, and how mitochondrial–endoplasmic reticulum interactions influence mitochondrial fission and fusion. These activities within the “cellular factory” may be associated with the metabolism of organs and tissues such as skeletal muscle and the heart, and metabolic abnormalities may lead to certain diseases. In-depth research into organelle function and activity may provide new perspectives for the study of certain metabolic disorders.

DNA and RNA are central to a variety of fundamental biological processes, serving to transmit genetic information for translation into proteins or to support gene regulation. Significant efforts have been made to “visualize” these genetic molecules, with sequencing technologies emerging as the most successful approach to date. However, the essence of sequencing lies in detection, computation, and statistical analysis; thus, despite its high accuracy, its results cannot be considered absolute. Furthermore, sequencing requires the fragmentation and reassembly of nucleic acid molecules, making it unsuitable for analyzing the genetic material of living cells.

The advent of super-resolution imaging technology has enabled nucleic acid imaging in living cells to achieve unprecedented resolution. As exemplified by the research of Professor Xiaowei Zhuang at Harvard University, she and her team have developed multiple technologies through breakthroughs in microscopy, including but not limited to single-molecule dynamics, nucleic acid–protein interactions, gene expression mechanisms, and nuclear–viral interactions. In the field of nucleic acid imaging, her team was the first to reveal the three-dimensional structure of human chromosomes, pioneered new techniques for detecting DNA–protein interactions, and constructed a spatial cell atlas of the hypothalamic preoptic area. She and her team have made outstanding contributions to the fields of single-molecule dynamics, nucleic acid–protein interactions, gene expression mechanisms, and nuclear–viral interactions.

Furthermore, Professor Xiaowei Zhuang, together with Professor David R. Walt and others, co-founded Vizgen, a biopharmaceutical company dedicated to developing next-generation spatially resolved single-cell transcriptomics. The company aims to push the boundaries of spatially resolved single-cell transcriptomics using super-resolution imaging technologies, thereby enabling further investigation into the molecular and cellular architecture of healthy and pathological tissues.

Overall, super-resolution imaging technology is a powerful tool for research at the level of living cells, and based on this technology, people are expected to gain insights into more secrets of biology.

Above are the “Top 10 Frontier Innovations of 2023,” summarized by Orange Bureau based on industry observations, interviews, and cutting-edge scientific research developments. Due to our limited understanding and preliminary knowledge, the “speculative” list may inevitably contain inaccuracies, omissions, or inappropriate descriptions. We welcome and appreciate your corrections. Equally important is the fact that technological innovation is a vast frontier; what we can see is likely not even the tip of the iceberg. If you have different perspectives or conjectures about future technologies, please feel free to share them in the comments.