Editor's Note: As the "nerve endings of the information age," sensors have permeated every critical field of the socio-economic landscape. Since October 2025, China Electronics News has invited Guo Yuansheng,Deputy Director of the Science and Technology Committee of the Central Committee of the Jiusan Society and Executive Vice Chairman of the Sensors and IoT Industry Alliance (SIA), to launch the column "Guo Yuansheng Discusses Sensors in Detail." The column focuses on eight major fields and scenarios: electric power, major equipment, intelligent manufacturing, smart agriculture, smart healthcare and big health, smart home appliances and consumer electronics, urban security, and low-altitude economy. It has successively published articles such as "Sensors Stationed at the Power Generation Side: The Cornerstone for the Stable Operation of the New Power System" and "Energy Storage Sensors Clarify Three Core Development Directions," which have received widespread attention and high praise from readers. This issue marks the second article in the smart healthcare field, focusing on medical sensors and elaborating on their application prospects, industrial status, and future recommendations to build industry consensus and promote industrial development.guo
Driven by multiple factors such as accelerating population aging, rising chronic disease rates, and the difficulties of home-based elderly care, healthcare is transitioning from "disease treatment" to the proactive medical direction of "health management," directly triggering a revolution in medical detection technology. Many disruptive innovative technologies and products have enriched precision medicine and personalized healthcare, achieving leapfrog upgrades and ultimate breakthroughs in "precise prediction and intelligent intervention." The intelligentization of the entire process integrating detection, diagnosis, and treatment poses higher requirements for sensing technology. As the foundation and core components of digital technology, medical sensors undertake key tasks such as physiological signal acquisition, pathological biomarker identification, environmental parameter monitoring, and the formation of health big data models, determining the efficiency, effectiveness, and outcomes of medical diagnostic data. Quantum sensing technology detects extremely weak physiological signals, pushing detection accuracy to new heights; gas sensor array technology constitutes multi-dimensional perception, achieving the leap from single gas detection to complex pathological feature identification; brain-computer interfaces open new pathways for the treatment of neurological diseases; and the deep integration of AI (Artificial Intelligence) and sensors completes the transition from "data acquisition" to "intelligent decision-making," upgrading sensors from signal acquisition to "intelligent diagnostic units." The synergistic effect of these cutting-edge technologies enables medical sensors to build and become the underlying foundation for the comprehensive digitalization of diagnosis and treatment.
I. Gas Sensor Array Technology: From "Single Detection" to "Multi-Dimensional Diagnosis"
Technological Breakthroughs: Overcoming the Performance Bottlenecks of Traditional Sensors
Single discrete gas sensors face two major pain points in medical detection applications: first, low detection accuracy, significantly affected by environmental temperature and humidity, interfering gases, and other factors. For instance, CO2 sensors in respiratory monitoring are easily interfered with by nitrogen and oxygen, often resulting in a detection error greater than 20%; second, single functionality, which cannot meet the needs of simultaneous multi-indicator detection under complex pathological conditions. The innovative combination of "array design + pattern recognition algorithms" fundamentally breaks through these bottlenecks. A sensor array made of 10-20 different sensitive materials forms an "electronic nose" similar to the human olfactory system. Each sensitive material has a unique response characteristic to specific gas components. When a mixed gas (such as human exhaled breath, intestinal metabolic gas, etc.) contacts the array, each sensor produces differentiated electrical signal changes, forming a "feature map" reflecting the gas composition. By analyzing, identifying, and matching these feature maps using machine learning algorithms (such as Support Vector Machine, Random Forest, Deep Learning, etc.), qualitative and quantitative detection of multiple components in the mixed gas can be achieved. Its characteristics are: first, anti-interference capability is improved by more than 50%; second, simultaneous multi-gas detection is realized with an accuracy rate of over 90%; third, by adjusting the combination of sensitive materials and algorithm models, it can be adapted to different detection scenarios, such as respiratory diagnosis, intestinal health assessment, and infectious disease screening.
Innovative Application Scenarios: From Non-Invasive Diagnosis to Health Management
1. Breath Cancer Detection: A Revolutionary Breakthrough in Non-Invasive Early Screening
Human exhaled breath contains hundreds of volatile organic compounds (VOCs), and the composition and concentration changes of these compounds are closely related to the physiological and pathological states of the human body. Studies have shown that the abnormal metabolism of cancer cells produces unique VOC "fingerprint maps"—the concentration of styrene in the exhaled breath of lung cancer patients is significantly elevated, the toluene content in the exhaled breath of gastric cancer patients is abnormal, and colorectal cancer is related to changes in indole and skatole in the exhaled breath. Gas sensor array technology provides a new, non-invasive, and convenient pathway for early cancer screening by accurately capturing these subtle changes.
The lung cancer breath detection device from the US company InspectIR employs a 16-channel gas sensor array, with sensitive materials covering various types such as metal oxide semiconductors and conductive polymers. Coupled with an algorithm model based on deep convolutional neural networks, it can effectively identify the characteristic VOCs maps in the exhaled breath of lung cancer patients. Clinical data shows that the detection rate for early-stage lung cancer (Stage I, Stage II) reaches 85%, a significant improvement over traditional chest X-rays, and it has received Emergency Use Authorization from the FDA for preliminary screening of high-risk populations for lung cancer.
Compared with traditional cancer screening methods, breath cancer detection technology has three significant practical advantages: first, non-invasiveness, requiring no puncture, blood drawing, or radiation scanning, which greatly reduces the psychological and physical burden on the examinees; second, rapid and high efficiency, with the entire detection process taking only 10 minutes to generate a preliminary report; third, low cost, with a single test costing approximately 200 RMB (data source: InspectIR's public clinical pricing and domestic agency channel quotes), which significantly improves the accessibility of cancer screening. It is especially suitable for large-scale population early screening and can be widely promoted for home use, possessing a massive market.
2. Precise Assessment of Intestinal Health: "Gas Probes" for Dysbiosis
The metabolic activities of the gut microbiota produce a large amount of gas, such as hydrogen, methane, and hydrogen sulfide. The composition ratio of these gases directly reflects the balance state of the microbiota—hydrogen is mainly produced by the metabolism of beneficial bacteria such as Bifidobacterium and Lactobacillus, methane is related to the number of methanogens, and excessive hydrogen sulfide indicates intestinal inflammation or overgrowth of harmful bacteria. Gas sensor array technology provides a brand-new non-invasive solution for intestinal health assessment through real-time monitoring of intestinal metabolic gases.
Through anal patch-type or oral capsule-type gas sensor arrays, metabolic gases in the intestine are continuously collected for 24 hours. The sensor array records the concentration change curves of key gases such as hydrogen, methane, and hydrogen sulfide in real time. AI algorithms, combined with the examinee's dietary records, symptom descriptions, and other information, conduct multi-dimensional analysis of the gas concentration data to determine the type of dysbiosis—for example, a hydrogen concentration >30 ppm and a methane concentration <10 ppm indicate the presence of Irritable Bowel Syndrome (IBS); a hydrogen sulfide concentration >5 ppm may predict intestinal inflammatory diseases such as ulcerative colitis and Crohn's disease; and simultaneous elevation of hydrogen and methane concentrations persistently exceeding the normal range is often related to Small Intestinal Bacterial Overgrowth (SIBO). This technology has been maturely applied in multiple specialized gastroenterology hospitals, incorporated into the auxiliary diagnosis system for intestinal diseases, and is beginning to be popularized in health management institutions.
3. Rapid Screening for COVID-19: The "Minute-Level" Defense Line for Respiratory Infectious Diseases
In the prevention and control of respiratory infectious diseases, rapid and convenient screening technology is key to blocking the transmission chain. COVID-19 infection leads to metabolic disorders in the human body, causing characteristic changes in the volatile metabolite map in exhaled breath—studies have found that the concentrations of compounds such as acetaldehyde and acetone in the exhaled breath of COVID-19 patients are significantly higher than those in healthy individuals. These changes can be accurately identified through gas sensor arrays, providing an effective means for large-scale rapid screening.
Technological Bottlenecks and Breakthrough Directions: The Leap from Laboratory to Industrialization
Gas sensor array technology still faces two core bottlenecks from laboratory R&D to large-scale industrialization: first, poor consistency of the sensor array. Due to subtle differences in the preparation processes of sensitive materials (such as thin-film deposition and nanomaterial dispersion), sensors in different batches or even within the same batch show significant differences in response characteristics. The response difference of some products can reach ±15%, leading to reduced repeatability of detection results; second, insufficient long-term stability. Sensitive materials are prone to aging and poisoning after repeated contact with gases, leading to sensor performance degradation. The accuracy of some products drops by more than 20% after 3 months of continuous use, which cannot meet the needs of long-term clinical monitoring.
In addition, multi-technology integration is becoming a new development trend for gas sensor array technology. For example, using MEMS (Micro-Electro-Mechanical Systems) technology can realize the miniaturization and low power consumption of sensors, laying the foundation for developing wearable breath detection devices; integrating spectroscopy technology with sensor arrays can further improve the specificity and accuracy of detection through the complementarity of spectral data and electrical signal data. These technological innovations are driving gas sensor arrays to extend from specialized medical devices to home health monitoring products, and the market application space continues to expand.
II. Quantum Sensors: The "Ultimate Weapon" for Ultra-High Precision Monitoring
Technical Principles and Performance Advantages: Breaking Through the Limits of Classical Physics
The core working principle of quantum sensors is to utilize the superposition and entanglement characteristics of quantum systems: a quantum system can be in a superposition of multiple states simultaneously, making it more sensitive to external signals; while quantum entanglement enables multiple quantum particles to form a correlated system, where measuring the state of one particle can deduce the states of other particles, thereby achieving signal amplification and noise reduction. In medical detection, commonly used types of quantum sensors include quantum magnetic sensors, quantum optical sensors, and quantum gravity sensors, each designed for specific weak physiological signals.
Compared with traditional sensors, the performance advantages of quantum sensors are reflected in two core dimensions: "ultra-high sensitivity" and "ultra-high precision." The sensitivity of quantum magnetic sensors can reach 1 pT (picotesla), which is more than 10 times that of traditional magnetic sensors, capable of detecting weak magnetic signals generated by cardiomyocytes and nerve cells; the precision of quantum gravity sensors can reach 1 μGal (microgal), capable of capturing minute gravity changes caused by density differences between tumor cells and normal cells; quantum optical sensors can achieve a cerebral cortex blood oxygen detection resolution at the 1 mm³ level, far exceeding traditional Near-Infrared Spectroscopy (NIRS) sensors (resolution of about 10 mm³). Overall, the sensitivity of quantum sensors is generally 1 to 3 orders of magnitude higher than that of traditional sensors, providing possibilities for early disease diagnosis and precise localization.
Another major advantage of quantum sensors is "non-invasiveness." Deep physiological information can be obtained without invasive methods such as puncture or implantation. For example, quantum magnetic sensors can detect the magnetic signals of cardiomyocytes in vitro, and quantum gravity sensors can non-invasively locate tumors in the body, significantly reducing the risks of medical detection and the pain of patients. This characteristic gives them irreplaceable advantages in the detection of special populations such as pediatrics and geriatrics.
Innovative Applications in the Medical Field: From Early Warning to Precision Treatment
1. Early Diagnosis of Cardiovascular and Cerebrovascular Diseases: "Magnetic Signal Probes" for Early Warning
Human cardiomyocytes generate weak biomagnetic signals during the excitation process, with an intensity only one ten-millionth of the Earth's magnetic field. Quantum magnetic sensors utilize the quantum entanglement effect to amplify this weak signal, achieving two-dimensional imaging of the heart's magnetic field through array arrangement to form a "magnetocardiogram." The magnetocardiogram can clearly reflect the electrophysiological activities of cardiomyocytes and precisely locate ischemic areas—even tiny ischemic foci with a diameter of less than 1 cm can be accurately identified. Compared with electrocardiograms, magnetocardiograms have higher spatial and temporal resolution, can distinguish different types of arrhythmias, and provide a basis for formulating personalized treatment plans.
The quantum magnetic diagnostic instrument developed by a German company adopts a superconducting quantum sensor array based on niobium-titanium alloy, coupled with a cryogenic refrigeration system to maintain a superconducting environment at -270°C, ensuring the ultra-high sensitivity of the sensors. Clinical data shows that when used for early screening of coronary heart disease, the detection time is only 15 minutes, with an accuracy rate of 92%, a significant improvement over traditional electrocardiograms (accuracy rate of about 75%). Currently, this equipment has been put into use in multiple specialized cardiac hospitals in European countries such as Germany and Switzerland for regular screening and postoperative follow-up of high-risk populations for coronary heart disease.
It also demonstrates unique advantages in the diagnosis of cerebrovascular diseases. For example, for the early diagnosis of cerebral infarction, quantum magnetic sensors can detect abnormal activities in brain tissue within 30 minutes after the blockage of cerebral blood vessels by detecting the weak magnetic signals of neurons in the brain, whereas traditional CT scans usually need 24 hours after the onset to clearly display the lesion. This early diagnostic capability can significantly reduce the disability and mortality rates of cerebral infarction, possessing extremely high clinical value.
2. Non-Invasive Brain Function Monitoring: The "Quantum Imager" Penetrating the Skull
Quantum dot optical sensors utilize the quantum size effect of quantum dot materials, where their fluorescence spectra can undergo characteristic shifts with changes in the blood oxygen concentration of the surrounding environment. Therefore, by analyzing the shift amount of the fluorescence signal, the blood oxygen saturation in different regions of the cerebral cortex can be calculated, thereby reflecting the neuronal activity status in that region—when neuronal activity is vigorous, the concentration of oxygenated hemoglobin increases, and the fluorescence signal shift amount increases; conversely, the shift amount decreases.
The quantum brain imaging headband developed by the US company Kernel integrates 32 quantum dot optical sensors, covering key areas such as the prefrontal and temporal lobes of the brain. It can collect cerebral cortex blood oxygen data in real time and convert the data into brain functional activity maps through AI algorithms. It precisely adjusts the dosage of antidepressant drugs and transcranial magnetic stimulation parameters for depression patients, increasing the treatment effective rate by 30%. In addition, it can also be used for early screening and early warning of Alzheimer's disease.
Compared with traditional brain function monitoring technologies, quantum dot optical sensors have three major advantages: first, non-invasive and convenient, with a headband design that is comfortable to wear, enabling 24-hour continuous monitoring without affecting the patient's normal life; second, relatively low cost, with the equipment price being only 1/20 of fMRI, suitable for use in primary medical institutions and homes; third, strong real-time performance, with a data sampling rate of up to 100 Hz, capable of capturing instantaneous changes in neuronal activity. Currently, this technology has been clinically applied in multiple fields such as psychiatry, neurology, and rehabilitation medicine, and is expected to become a core tool for brain science research in the future.
3. Precision Tumor Localization: The "Non-Invasive Navigator" Based on Gravity Differences
The tumor localization principle of quantum gravity sensors is based on the "density difference effect": the proliferation speed of tumor cells is much faster than that of normal cells, and the cell density is higher (for example, the density of liver cancer cells is about 1.06 g/cm³, while the density of normal liver cells is about 1.03 g/cm³). This density difference leads to minute changes in the gravity field of the tumor area. By measuring this minute gravity change and combining it with 3D reconstruction algorithms, quantum gravity sensors can determine the location and size of the tumor. Unlike imaging technologies, the detection of quantum gravity sensors is not affected by factors such as tissue density and bone shielding. It can penetrate deep human tissues to achieve precise localization of tumors in hidden areas such as the abdominal and pelvic cavities.
This technology can monitor the positional changes of tumors caused by breathing and heartbeat in real time during tumor surgery, guiding surgical instruments to precisely remove the tumor and reducing damage to normal tissues; in radiotherapy, it can adjust the radiation dose distribution according to the real-time position of the tumor, avoiding treatment deviations caused by tumor displacement. In addition, this technology does not require the use of contrast agents and radiation sources, has no side effects on patients, and is suitable for long-term follow-up monitoring.
Industrialization Progress and Challenges: From Technological Breakthroughs to Market Popularization
Given the continuous breakthroughs in quantum sensor technology, the global medical quantum sensor industry has entered an accelerated development stage. The top 5 medical device companies—Johnson & Johnson, Siemens, Abbott, Medtronic, and Philips—have all established dedicated quantum sensor R&D centers, investing heavily in technological R&D and clinical validation. For example, Siemens Healthineers is collaborating with Heidelberg University in Germany to develop cardiovascular and cerebrovascular diagnostic equipment based on quantum magnetic sensors; Johnson & Johnson is focusing on the application of quantum optical sensors in tumor diagnosis, planning to launch its first commercial product before 2026.
As the technology continues to mature and costs gradually decrease, the market prospects for quantum medical sensors are extremely broad. According to forecasts by market research firm Grand View Research, the global quantum medical sensor market size will grow from $1.2 billion in 2024 to $8.5 billion in 2030, with a compound annual growth rate of 38.5%. Among them, cardiovascular and cerebrovascular diagnosis, brain function monitoring, and tumor localization will become the three core application fields, accounting for more than 70% of the market size.
III. Brain-Computer Interface Sensors: The "Intelligent Bridge" Connecting Nerves and Machines
Classification of Technical Pathways: Gradient Selection from Non-Invasive to Invasive
Establishing a direct communication channel between the brain and external devices to realize the bidirectional conversion of neural signals and machine commands, the brain-computer interface (BCI) sensor is the core sensing unit of this channel, responsible for acquiring neural electrical signals or metabolic signals generated by the brain. According to the contact method between the sensor and brain tissue, BCI sensors can be divided into three major technical pathways: non-invasive, semi-invasive, and invasive. Different pathways have their own advantages and disadvantages in terms of signal accuracy, wearing convenience, and invasiveness, and are suitable for different medical scenarios.
The core characteristic of non-invasive sensors is that they are worn on the surface of the scalp without surgical implantation, possessing advantages such as simple operation, high safety, and low cost. Early EEG sensors required the use of conductive paste to enhance signal conduction, making wearing and cleaning inconvenient. In contrast, the new generation of non-invasive sensors adopts dry electrode design, closely contacting the scalp through micro-nano structures, achieving stable signal acquisition without the need for conductive paste.
Semi-invasive sensors involve surgically implanting the sensor array under the skull and on the surface of the cerebral cortex without penetrating the brain parenchyma. This design not only avoids direct damage to brain tissue but also reduces the interference of the skull and scalp on signals. The signal accuracy is 10 times higher than that of non-invasive sensors, capable of capturing the electrical activity of local neuronal clusters in the cortex. Semi-invasive sensors usually use flexible thin-film substrates, such as polyimide materials, which can closely fit the cortical surface and reduce mechanical stimulation to brain tissue. However, they still require craniotomy, posing certain surgical risks, and long-term implantation may trigger inflammatory responses.
Invasive sensors involve surgically implanting ultra-fine probes into deep brain nuclei to directly contact neurons, capable of recording the firing signals of single neurons with signal accuracy reaching the microvolt level. The diameter of neural probes is usually less than 100 μm, only 1/10 of the diameter of a hair, minimizing damage to brain tissue. Some advanced neural probe sensors also integrate stimulation functions, releasing electrical pulses to neurons while acquiring signals, realizing the integration of "sensing-stimulation." However, invasive sensors have the highest surgical risk, and long-term implantation is prone to triggering glial scar formation, affecting the stability and service life of the sensors.
The selection of the three major technical pathways requires comprehensive consideration of clinical needs, patient tolerance, and technological maturity: non-invasive sensors are suitable for non-therapeutic scenarios such as rehabilitation training and health monitoring; semi-invasive sensors are suitable for scenarios requiring higher signal accuracy, such as epilepsy warning and motor function reconstruction; invasive sensors are mainly used for the precision treatment of neurological diseases such as Parkinson's disease and Alzheimer's disease.
Medical Application Scenarios: Reshaping the Treatment Model for Neurological Diseases
1. Non-Invasive: The Rehabilitation "Exoskeleton" for Stroke Patients
Stroke is one of the main causes of limb dysfunction. Globally, about 15 million people suffer from stroke each year, among whom about 70% of patients are left with varying degrees of limb paralysis. Traditional rehabilitation training relies on the manual assistance of physical therapists, with limited training effects and low efficiency. In contrast, rehabilitation systems based on non-invasive BCI sensors identify patients' motor intentions and control smart exoskeletons to complete movements, realizing "mind-driven" rehabilitation training and significantly improving rehabilitation outcomes.
The stroke rehabilitation system (trade name: ReWalk Restore) jointly developed by the Massachusetts Institute of Technology and Boston Dynamics in the US consists of a flexible EEG headband, a smart lower limb exoskeleton, and rehabilitation training software. The EEG headband integrates 16 dry electrode sensors, which can accurately identify patients' motor intentions such as "standing" and "walking," controlling the exoskeleton to assist patients in completing gait training. Clinical research data shows that stroke patients using this system for rehabilitation training of 1 hour per day, 5 times a week, achieved a recovery rate of 65% in independent arm-raising ability and 58% in independent walking ability after 3 months, representing improvements of 28% and 30% respectively over traditional rehabilitation training. As of 2024, this system has been put into use in over 500 rehabilitation medical institutions in 32 countries globally, benefiting hundreds of thousands of stroke patients.
2. Semi-Invasive: The "Warning Guardian" for Epilepsy Patients
There are approximately 65 million epilepsy patients globally. Its core characteristic is the sudden abnormal discharge of brain neurons, leading to transient brain dysfunction. Epileptic seizures are sudden and unpredictable, prone to causing accidental injuries (such as falls and drowning). Traditional treatment methods (such as drugs and surgery) cannot completely control seizures, and about 30% of patients suffer from drug-resistant epilepsy. The epilepsy warning system based on semi-invasive BCI sensors sends warning signals in advance by monitoring abnormal brain electrical discharges in real time, buying time for patients to avoid risks, while simultaneously linking with neural stimulators to inhibit seizures, significantly reducing the frequency of seizures.
3. Invasive: The "Tremor Terminator" for Parkinson's Disease Patients
There are over 10 million Parkinson's disease patients globally. Its main symptoms include limb tremors, muscle rigidity, and bradykinesia, severely affecting patients' quality of life. Traditional treatment methods mainly involve taking drugs such as levodopa, but long-term medication is prone to drug resistance and side effects. Although Deep Brain Stimulation (DBS) surgery can effectively improve symptoms, traditional DBS lacks real-time signal feedback, and stimulation parameters cannot be dynamically adjusted according to changes in the patient's condition. The Parkinson's disease treatment system based on invasive BCI sensors achieves precision treatment by dynamically adjusting stimulation parameters through real-time monitoring of neuronal activity in deep brain nuclei.
Sensors have realized "personalized precision treatment" in Parkinson's disease treatment: dynamically adjusting parameters based on real-time monitored neuronal signals ensures the precision and effectiveness of the treatment. In addition, it also possesses remote monitoring functions. Doctors can view patients' neuronal activity data and treatment effects in real time via the network and remotely adjust treatment plans, improving the accessibility of medical services.
Technological Breakthrough Directions: Enhancing Stability and Integration
BCI sensors still face four major core technological bottlenecks in the medical field.
1. Long-term stability issues. After invasive neural probe sensors are implanted into the brain, brain tissue produces an immune response, forming glial scars that encapsulate the probe, leading to poor contact between the sensor and neurons, and signal quality degrades over time—the stable working time of traditional neural probes is usually no more than 1 year. To solve this problem, the core breakthrough direction in the industry is to develop flexible neural probes, using flexible biocompatible materials such as polyimide and hydrogels to make the probe substrate. Their elastic modulus is close to that of brain tissue, which can reduce mechanical stimulation to brain tissue and lower the formation speed of glial scars. For example, the hydrogel-based neural probe developed by Harvard University in the US uses hydrogel materials similar in composition to brain tissue, reducing glial scar formation by 60% after implantation and extending the stable working time to over 5 years.
2. Difficulties in multi-channel integration. The number of detection channels in traditional neural probe sensors is usually 8-16, which can only record neuronal activity in local areas and cannot achieve the acquisition of cooperative signals from multiple brain regions. Given the development of micro-nano processing technology, the channel integration capability of a single neural probe sensor has been significantly improved—currently, the most advanced neural probes can integrate 1024 detection channels. Through array arrangement, they can simultaneously record neuronal activity in multiple brain regions, providing possibilities for analyzing brain functional networks. For example, the "nanowire neural probe" developed by Stanford University in the US uses nanowire arrays as detection electrodes, integrating 1024 channels on a probe with a diameter of 100 μm. The signal sampling rate reaches 10 kHz, clearly recording the cooperative firing patterns of neurons in different brain regions.
3. Challenges in wireless transmission technology. Sensors need to be connected to external devices via wires. The presence of wires not only affects patients' daily lives but may also trigger infection risks. In recent years, wireless communication technologies such as Near Field Communication (NFC), Bluetooth Low Energy (BLE), and Radio Frequency Identification (RFID) have been widely applied in BCI sensors, realizing wireless data transmission. In the future, with the application of 5G and millimeter-wave communication technologies, the data transmission rate and distance of BCI sensors will be further improved, laying the foundation for telemedicine and real-time intervention.
4. Innovation in biocompatible materials. Materials such as polyethylene glycol and chitosan can reduce the immune response of sensors to brain tissue while improving the anti-corrosion ability of sensors. The development of degradable neural probes provides new options for short-term treatment scenarios—these probes can naturally degrade in the body after completing treatment tasks, without the need for secondary surgery for removal, further reducing surgical risks.
IV. Deep Integration of AI and Sensors: From "Data Acquisition" to "Intelligent Decision-Making"
Three Core Pathways for AI to Enhance Sensor Performance
Through real-time processing and intelligent analysis of sensor data via algorithms, the leap from "data acquisition" to "intelligent decision-making" is realized, significantly enhancing the application value of sensors. The deep integration of AI and sensors has three core pathways to improve the performance of medical sensors: data denoising and calibration, pattern recognition and diagnosis, and prediction and intervention, forming a complete closed loop of "data processing-analysis-decision-making."
Data denoising and calibration are the fundamental links for AI to enhance sensor performance. During data acquisition, medical sensors are easily affected by various interference factors. Through AI algorithms, especially Convolutional Neural Networks (CNN), learning from a large number of "noisy data-clean data" samples can automatically identify and separate noise components in the data, achieving precise denoising. For example, addressing the motion artifact problem of home blood oxygen sensors, CNN-based denoising algorithms can improve blood oxygen detection accuracy from ±3% to ±1.5%, obtaining stable detection results even when patients are in motion.
In addition to denoising, AI algorithms can also realize automatic calibration of sensors. By establishing a sensor drift model, the performance changes of the sensor can be monitored in real time, and calibration parameters can be automatically adjusted. For example, blood glucose sensors use historical data and reference data to correct detection errors in real time, extending the sensor's calibration cycle from 1 week to 1 month while maintaining detection accuracy within ±2%.
Pattern recognition and diagnosis are the core application links in the integration of AI and sensors. By constructing deep neural network models, high-dimensional features in the data can be automatically extracted to achieve precise disease diagnosis. For example, in the heart rate data collected by electrocardiogram sensors, an algorithm model combining Recurrent Neural Networks (RNN) and attention mechanisms can automatically identify 12 common types of arrhythmias with an accuracy rate of 98%, a significant improvement over manual diagnosis.
Prediction and intervention represent the advanced application stage of AI and sensor integration. Based on the stability of data denoising and calibration and the precision of pattern recognition and diagnosis, it realizes the transition from "passive diagnosis" to "proactive health management." Traditional medical sensors can only reflect the current physiological state and cannot predict the development trend of diseases. However, AI algorithms can establish disease development models by analyzing long-term accumulated sensor data, predict future health risks, and provide intervention suggestions. Commonly used prediction algorithms include Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost). These algorithms possess powerful time-series data processing capabilities and can capture long-term change patterns of physiological parameters. For example, by continuously collecting blood glucose sensor data from diabetic patients for 3 months, an LSTM-based prediction model can analyze blood glucose fluctuation trends and predict the risk of diabetic nephropathy with an accuracy rate of 82%, providing a basis for early intervention.
Technological Challenges and Future Directions: The Evolution Path of AI + Sensors
Despite the significant achievements in the integration of AI and medical sensors, it still faces multiple technological challenges such as data quality, algorithm reliability, and system integration. These challenges directly restrict the large-scale popularization and in-depth application of the technology. Meanwhile, given the continuous development of technology, new evolutionary directions are gradually becoming clear, pointing out the path for industry development.
System integration and real-time requirements are the engineering technological challenges faced by AI + sensors in clinical applications. In terms of system integration, the interface protocols of different types of medical sensors are not unified, and the docking of AI algorithms with existing hospital information systems (such as HIS, LIS, PACS) is difficult, leading to unsmooth data flow and forming "information silos." To solve this problem, the industry is promoting the popularization of medical device interconnection standards, such as the HL7 FHIR standard, to realize seamless docking between sensor data and hospital information systems. Meanwhile, modular AI algorithm platforms are being developed to support rapid access and analysis of different sensor data. In terms of real-time performance, some clinical scenarios (such as surgical navigation and closed-loop intervention) have extremely high requirements for the response time of AI algorithms, requiring data processing and decision-making to be completed at the millisecond level. Traditional cloud-based AI computing models cannot meet real-time requirements due to network latency. Edge computing technology has emerged—deploying AI algorithms on sensor terminals or local servers, processing data locally, and shortening response times from the second level to the millisecond level, meeting the needs of clinical real-time intervention. For example, an AI blood glucose closed-loop system with an edge computing architecture can immediately trigger insulin pump intervention when blood glucose is abnormal, with a response time of less than 100 milliseconds.
In the future, the integration of AI and medical sensors will evolve in three major directions: "multi-modal fusion," "edge-cloud collaboration," and "autonomous learning." Multi-modal fusion not only refers to the fusion of data from different types of medical sensors but also includes the fusion of multi-dimensional data such as physiological data, clinical texts, and genetic data. Through AI algorithms, deep correlations between data are mined to improve the comprehensiveness and accuracy of diagnosis. The edge-cloud collaboration model combines the advantages of edge computing and cloud computing: terminal devices realize real-time data processing and rapid decision-making through edge AI, while the cloud platform uses massive data for model training and updating, pushing the optimized models to the terminal via OTA (Over-The-Air) methods to achieve continuous improvement of system performance. Autonomous learning AI algorithms possess self-iteration capabilities, continuously learning new data samples in clinical applications and automatically optimizing model parameters. They can adapt to new disease patterns and patient groups without manual intervention, significantly reducing model maintenance costs.
V. Global Industrial Landscape and Development Opportunities for Chinese Enterprises
Global Industrial Landscape: Coexistence of Giant Dominance and the Rise of Innovative Enterprises
The global medical sensor industry presents a "pyramid" structure. The top of the pyramid is dominated by international medical device giants, holding core technologies and the high-end market; the middle consists of multinational enterprises with certain technological strength, focusing on segmented fields; the base comprises a large number of small and medium-sized innovative enterprises, taking technological breakthroughs as their core competitiveness and continuously impacting the mid-to-high-end market. Meanwhile, the global industrial layout shows obvious regional characteristics. The US, Europe, and Japan occupy leading positions in technological R&D and industrial scale, while emerging markets such as China and India have become core engines for industrial growth relying on their huge population base and policy support.
The US is the leader in the global industry, possessing absolute advantages in technological R&D, innovation capability, and industrial ecology. The industrial advantages of the US are reflected in three levels: first, leading core technologies, holding core patents for cutting-edge technologies such as quantum sensors and BCI sensors. For instance, the number of patents in the field of invasive BCI sensors by companies like Medtronic and Qualcomm accounts for over 60% of the global total; second, a perfect industrial ecology, forming a complete closed loop of "university R&D-enterprise transformation-capital support-clinical validation." Scientific research achievements from top universities such as Stanford University and the Massachusetts Institute of Technology can be rapidly industrialized through start-up enterprises, and venture capital institutions in Silicon Valley provide sufficient financial support for innovative enterprises; third, strong market demand, with US healthcare expenditure accounting for 18% of GDP. The demand for precision medicine and personalized health management drives the continuous upgrading of medical sensor technology. Representative US enterprises include Medtronic (BCI sensors), Abbott (blood glucose sensors), and Google Health (AI + medical sensors). These enterprises not only occupy the high-end market but also continuously expand technological boundaries by acquiring innovative enterprises.
Europe has unique advantages in the precision manufacturing and clinical application of medical sensors, especially performing outstandingly in fields such as quantum sensors and gas sensor arrays. The industrial characteristics of Europe are "technological precision" and "clinical orientation": the precision manufacturing technologies of Germany and Switzerland ensure high reliability and stability of sensors. For example, the failure rate of medical gas sensor arrays from Germany's Bosch is less than 0.1%. Meanwhile, Europe possesses a perfect clinical research system, where innovative technologies can be quickly validated through multi-center clinical trials, accelerating the product launch process. Representative European enterprises include Siemens Healthineers in Germany (quantum magnetic sensors), Roche in Switzerland (diagnostic sensors), and Scentech Medical in Israel (breath detection sensors). In addition, although Europe's Medical Device Regulation (MDR) raises the market access threshold, it also promotes the industry's development towards high quality and standardization, enhancing the global competitiveness of European enterprises.
Japan has significant advantages in consumer-grade medical sensors and miniaturization technology fields. Relying on its strong electronic manufacturing industry base, it has realized the miniaturization, low power consumption, and low cost of medical sensors. The industrial characteristic of Japan is "civilian popularization," integrating medical sensor technology into consumer electronic devices. Products such as Omron's electronic blood pressure monitors and Panasonic's home blood oxygen monitors occupy an important share of the global consumer-grade market. Meanwhile, driven by the needs of an aging society, Japan has developed a large number of sensor products suitable for elderly health management, such as wearable fall detection sensors and physiological parameter monitoring sensors for long-term care. Representative Japanese enterprises include Omron (blood pressure, blood glucose sensors), Hitachi (image sensors), and Sony (quantum dot optical sensors). These enterprises possess strong competitiveness in the mid-to-low-end market through scaled production and cost control.
Besides the aforementioned traditional industrial powers, a group of innovative enterprises focusing on cutting-edge technologies has also emerged in the global medical sensor industry. These enterprises take technological breakthroughs as their core, rapidly rising in segmented fields and becoming a new force driving industrial development. For example, the US company Kernel focuses on quantum brain imaging sensors, and its developed headband-style brain function monitoring equipment has opened a new market for brain health management; China's iFLYTEK has laid out in the field of AI + medical sensors, developing a mental health monitoring system based on voice and physiological signals; Canada's Deep Genomics combines AI with genetic sensors to achieve precise diagnosis of genetic diseases. Relying on flexible mechanisms and focused R&D, these innovative enterprises can often break through giant monopolies in segmented fields, becoming an important driving force for industrial innovation.
Current Status of China's Industry: Dual Empowerment of Policy Drive and Technological Breakthroughs
In recent years, driven by multiple factors such as policy support, market demand, and technological innovation, China's medical sensor industry has achieved rapid development, and the trend of transformation from "import dependence" to "independent innovation" is increasingly obvious. Currently, China's medical sensor industry has formed three major industrial clusters: "the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim." The Yangtze River Delta region focuses on technological R&D and high-end manufacturing, gathering enterprises such as United Imaging Healthcare and Fosun Pharma; the Pearl River Delta region relies on its electronic manufacturing advantages, focusing on the scaled production of consumer-grade medical sensors; the Bohai Rim region, relying on university and research institution resources, has advantages in cutting-edge technological R&D, such as the quantum sensor R&D by the Institute of Physics, Chinese Academy of Sciences.
Policy support is an important guarantee for the development of China's medical sensor industry. At the national level, the "14th Five-Year Plan" lists "high-end medical equipment and innovative drugs" as key development areas, and medical sensors, as core components, are included in the support scope. The "Outline of the 'Healthy China 2030' Plan" proposes strengthening the R&D of health monitoring technologies and promoting the application of medical sensors in fields such as chronic disease management and elderly health services. The "Special Review Procedures for Innovative Medical Devices" issued by the National Medical Products Administration provides a fast-track approval channel for innovative medical sensor products, shortening the product launch cycle. At the local level, regions where industrial clusters are located have successively introduced supporting policies. For example, Shanghai has set up a special fund for medical sensors, providing enterprises with R&D subsidies and financing support; Guangdong Province promotes the integrated development of "medical sensors + intelligent manufacturing," building industrial parks to provide enterprises with production supporting services. These policies have formed a full-chain support system from R&D and transformation to industrialization, creating a good environment for industrial development.
Market demand is the core driving force for the growth of China's medical sensor industry. China possesses the world's largest healthcare market. Accelerating population aging (latest data for 2025 shows: the proportion of the population aged 60 and above reaches 21.8%), a huge base of chronic disease patients (over 300 million hypertension patients), and enhanced health management awareness have jointly generated huge demand for medical sensors. In the clinical field, tertiary hospitals have strong demand for high-end medical sensors, such as quantum magnetic sensors and AI image sensors. In the consumer field, the home health monitoring equipment market is growing rapidly. In 2024, the market size of China's wearable medical devices reached 80 billion RMB, and it is expected to exceed 120 billion RMB in 2026, driving an explosive demand for consumer-grade sensors. Meanwhile, the standardized construction of primary medical institutions also provides a broad market for mid-to-low-end medical sensors, such as the year-by-year increasing procurement volume of basic physiological parameter sensors by township health centers.
Development Opportunities and Breakthrough Paths for Chinese Enterprises
Facing the global industrial landscape and its own development status, Chinese medical sensor enterprises have ushered in three major development opportunities: policy dividends, market upgrading, and technological integration. At the same time, they need to achieve breakthroughs and enhance global competitiveness through three major paths: "technological tackling," "industrial chain integration," and "international layout."
The opportunity of policy dividends is reflected in the national key support for high-end medical equipment and the promotion of domestic substitution. Given the implementation of medical insurance cost-control policies, medical institutions have increased their demand for cost-effective domestic medical sensors, providing market entry points for domestic enterprises. The national "choke point" technological tackling plan includes core components of medical sensors in the tackling scope, providing enterprises with R&D funding and technological support. The opening of the medical device innovation channel accelerates the launch process of domestic innovative products, helping enterprises seize market opportunities.
The opportunity of market upgrading stems from the structural changes in China's healthcare market. The transition from "disease treatment" to "health maintenance" promotes the emergence of new scenarios such as health monitoring and chronic disease management. These scenarios pose new demands for the miniaturization, wearability, and intelligence of medical sensors, providing opportunities for differentiated competition for Chinese enterprises.
The opportunity of technological integration comes from the innovation space brought by the intersection of multi-disciplinary technologies. China's technological advantages in fields such as AI, 5G, and IoT (Internet of Things) provide support for the innovation of medical sensors. Integrated technologies such as AI + sensors and IoT + sensors have become breakthrough points for Chinese enterprises.
In terms of breakthrough paths, technological tackling is the core, requiring focus on two major directions: core components and cutting-edge technologies. Addressing the issue of reliance on imports for core components, enterprises should join hands with universities and research institutions to establish industry-university-research cooperation platforms, focusing on tackling "choke point" technologies such as sensor chips, sensitive materials, and packaging processes to achieve independent and controllable production.
Industrial chain integration is the key to enhancing the overall competitiveness of the industry. Chinese enterprises should leverage the advantage of a complete industrial chain to build a collaborative industrial chain system of "materials-design-manufacturing-application." Upstream, sensor material suppliers can be integrated through investment, mergers, and acquisitions to ensure the stability and cost advantages of raw material supply. Midstream, cooperation between sensor manufacturers and chip design enterprises can be promoted to realize the integrated development of sensors and chips, improving product performance. Downstream, synergy with complete medical device enterprises needs to be strengthened, customizing sensor products according to complete machine demands to form a bundled cooperative relationship.
International layout is an inevitable choice for Chinese enterprises to expand market space and enhance global competitiveness. Enterprises should adopt an internationalization strategy combining "differentiated entry" and "technological cooperation": in developing country markets, promote mid-to-low-end medical sensor products relying on cost-performance advantages to seize market share; in developed country markets, integrate into the global innovation system and enhance technological levels through technological cooperation with local enterprises and the establishment of R&D centers.
Conclusion
As the core sensing unit of the healthcare industry, the technological development and industrial transformation of medical sensors profoundly affect the accuracy of medical diagnosis, the efficiency of health management, and the accessibility of medical services. Through the systematic analysis of four cutting-edge technological pathways—gas sensor arrays, quantum sensors, BCI sensors, and the integration of AI and sensors—combined with the analysis of typical global industrial cases and landscapes, the technological development context and industrial transformation trends in the field of medical sensors are clearly demonstrated. Multi-disciplinary technological integration is driving medical sensors to leap from "basic signal acquisition" to "precise intelligent decision-making," entering a brand-new development stage characterized by "precision, intelligence, minimal invasiveness, and integration."
The development of medical sensors is the support and pathway for the transformation of medical technology from "extensive" to "precise" and from "disease treatment" to "health maintenance." Given the continuous progress of technology and the continuous upgrading of the industry, medical sensors will play an increasingly important role in the path of safeguarding human health. For Chinese enterprises, grasping the trend of technological integration, tackling core technologies, and integrating industrial chain resources will become the key to achieving breakthroughs in the global medical sensor industry, injecting core momentum into the "Healthy China 2030" goal.
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Central Committee of the Jiusan Society and Executive Vice Chairman of the Sensors and IoT Industry Alliance (SIA). Editor | Yang Pengyue. Art Editor | Ma Liya. Supervisor | Zhao Chen.