Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection Free

Electrochemical Immunosensing (EI) has received much attention due to the need for fast and reliable methods to detect drugs, proteins, hormones, as well as pesticides, phenols, and antibiotics at concentrations as low as 10–12 mol l⁻¹ in clinical analysis and environmental detection,¹ because it combines the inherent specificity of immune reactions with high sensitivity of electrochemistry.^2–4 EI has become one of the most promising technologies in the field of biosensors.⁵ EI usually consists of three main parts: a molecular recognition layer, a sensor, and a signal generator.^6,7 The molecular recognition layer is generated by immobilizing bioreceptors with specific binding characteristics for the target analyte.^8,9 Among them, antibodies are one of the most widely used molecules because of their high specificity, affinity, and ease of production. EI is based on the biological sensing of the antigen-antibody binding, and the detection of the electron signal changes caused by the impediment of the electron flow during the quantization of the antigen-antibody interactions can achieve quantitative and qualitative analysis of different antibodies or antigens. Due to the high sensitivity, miniaturization compatibility, and integration of electrochemical detection, as well as the high specificity and strong binding force of immunological analysis, EI has become an important tool in many fields such as clinical diagnosis,¹⁰ disease prevention and treatment,^11,12 port inspection,¹³ and food safety.¹⁴ 

In recent years, there has been a growing demand for simultaneously detecting multiple analytes to improve analysis performance and efficiency, particularly in view of the rapid development of technologies that greatly enhance the potential for comprehensive analysis of complex biological systems.¹⁵ Thus, EI multi-component detection has become a major focus of EI research and has the potential for rapid, and low-cost detection. For example, Najjar et al.¹⁶ applied an EI method to achieve rapid and accurate detection of two substances, namely, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) RNA and the virus's serological host antibodies, simultaneously. Abdulkarim et al.¹⁷ introduced an electrochemical immunosensor fabrication method for the simultaneous detection of multiple components, including acid α-glucosidase (GAA), β-Glucocerebrosidase (GBA), and galactocerebrosidase (GALC). After spiking human serum samples, the immunosensor exhibited good selectivity, sensitivity, and recovery. However, traditional EI still has limitations and drawbacks, such as expensive equipment, slow response, and complex operating procedures, which have hindered the practical application of multi-component detection.^18,19 Brandão et al.²⁰ introduced a new method for detecting multiple foodborne pathogenic bacteria. Antibody-based magnetic extraction for individual bacteria from contaminated food matrices and interfering compounds has been shown to enhance immunological detection performance. However, these platforms still require bulky instruments for magnetic separation and electrode reading, limiting the portability and cost-effectiveness of multi-component detection.²¹ Moreover, in complex environments with varying ionic strength, temperature, pH, and other factors, actual samples may contain many interfering factors. Ge et al.²² investigated the use of an artificial neural network (ANN) algorithm to establish an intelligent output machine learning (ML) model, which adapts to pH changes in the microenvironment, to achieve intelligent analysis of mycophenolic acid (MPA) in silage under varying pH microenvironments. However, EI systems are inevitably influenced by sample matrices and operational conditions.²³ Moreover, the electrodes or modified electrodes used for EI corrode over time.²⁴ Furthermore, EI requires specific antibodies or receptors for each target molecule, which can cause non-specific adsorption of interfering substances. Selectivity-enhancing strategies have been proposed, such as the use of aptamers²⁵ or blocking agents,²⁶ but they still cannot effectively solve the precise decoupling of complex multi-component signals. In summary, there is an urgent need to address the following two key issues:

1. Lack of a low-cost and portable detection platform: Currently, most EI detection instruments require expensive equipment and complex operating procedures, which are not suitable for fast detection of multiple components in the field, remote areas, or simple laboratories.²⁷ Therefore, there is an urgent need to develop low-cost, miniaturized, and portable multi-component detection platforms to be more widely applied in practical production and life.

2. Difficulty in eliminating batch differences and solving the dilemma of precise decoupling of multi-component signals in EI: EI mostly uses screen-printed electrodes with small surface areas as the main transducer components. The concept of batch differences is defined as follows: In electrochemistry, batch differences refer to the performance differences of electrodes or electrolytes in the same batch in different experiments caused by factors such as experimental conditions, electrode materials, and electrolytes.^28,29 Batch differences can lead to changes in the response characteristics of EI, which affects the accuracy and repeatability of multi-component detection. In addition, current EI multi-component detection has inherent limitations. The concept of multi-component detection is defined as follows: In electrochemical analysis, multi-component detection refers to the simultaneous detection of the content of multiple substances.³⁰ Different substances affect electron transfer pathways and electrode surface reactions in different ways. Meanwhile, the signals between multiple components have mutual interference or overlap, making it difficult to accurately decouple them.

In the search for solutions to key issue 1, the biochip as an EI platform has received widespread attention. Biochip is a new emerging technology in the fields of biomedical,³¹ pharmaceutical,³² environmental,³³ and food analysis.³⁴ It is a new type of biosensor system that integrates microfabrication, microsensors, microfluidics, and microassembly technologies, with advantages such as high detection sensitivity, short routine detection time, small sample requirements, and diversified detection modes.³⁵ The significant feature of the biochip is fused with EI, providing a portable platform for multi-component detection in practical applications, which makes EI multi-component detection have a wider application prospect in point of care (POC) applications.³⁶ However, the data generated by biochips are often huge and complex and require efficient and accurate data processing and analysis to extract useful information.

In the search for solutions to key issue 2, AI technology has gradually entered the researchers' vision. Due to its powerful data processing and analysis capabilities and excellent simulation and learning abilities, AI technology is considered one of the most promising technologies in recent years and plays an important role in many key research areas, such as bioinformatics, biochemistry, medicine, aquaculture, food safety, agricultural production, and agricultural product testing.^37–39 The rapid development of EI based on AI not only helps us to eliminate the batch differences of EI⁴⁰ but also enables accurate decoupling of multi-component signals automatically, which is a necessary means to achieve EI multi-component detection. Yan et al.⁴¹ achieved selective recognition of multi-component gases by using a single optical fiber sensor and applying principal component analysis (PCA) based on near-infrared (NIR) mechanism and wavelength-localized thermal emission effect in ML. Zhang et al.⁴² extracted features based on three-dimensional analysis space of near-infrared (NIR) spectral characteristics in the field of production process control, using third-order tensor robust principal component analysis (TRPCA) algorithm and establishing a qualitative recognition model based on support vector machine (SVM), which can detect the quality of complex multi-component mixtures. Turek et al.⁴³ realized multi-component heavy metal solution measurement in the field of heavy metal chemical sensing using fuzzy logic analysis method. The cross-sensitivity of multi-components in these fields is akin to the overlapping sensitivity of multi-components in EI sensors to some extent. Therefore, the application of AI in the multi-component detection in other fields has great implications and inspirational significance for exploring its application in EI biochips.

The cross-disciplinary integration inspires innovative thinking and provides new solutions, thus promoting innovation and rapid development in the field of biomedicine.⁴⁴ The integration of EI, biochips, and AI provides enormous potential for creating new tools for disease diagnosis, drug development, and environmental monitoring.⁴⁵ The EI system integrated with biochip and AI technology can rapidly obtain the signal of biomarkers on the biochip and transmit it to the AI computing unit for feature extraction and data analysis, thus obtaining more accurate, comprehensive, and faster multi-component detection results, as shown in Fig. 1. Therefore, only by integrating various disciplines and creating new ideas and methods can more rigorous and practical solutions be provided for complex diagnostic issues such as multi-component detection, making greater contributions to human physical and psychological health.

This paper reviews and looks ahead at the EI method integrating AI and biochip technology, foreseeing the potential of the three technologies to solve the problem of multi-component detection in the diagnosis of biomolecules and greatly promote the development of the diagnostic field. Section I presents the characteristics, deficiencies, and limitations of traditional EI and then introduces the advantages of AI technology and biochip technology, bringing hope for EI multi-component detection. Section II introduces the application and advantages of biochips in EI. Section III discusses how AI technology solves the problem of batch difference elimination and multi-component signal decoupling in EI multi-component detection. Section IV is the conclusion and outlook of the paper.

The combination of biochips and EI can not only improve signal quality and accelerate detection speed but also effectively supplement the limitations of traditional EI in single detection, making it applicable to the scene of multi-component detection.⁴⁶ In terms of biochip design, it is crucial to prepare appropriate sensors. The detection performance of biochips can be improved by changing electrode materials, using novel functional molecules, and optimizing biological reactions. Traditional microfluidic chips, as shown in Fig. 2(a), are plastic sheets prepared by confidential machining and micro-injection;^47,48 paper-based microfluidic chips, as shown in Fig. 2(b), are microfluidic chips that use paper (such as filter paper and nitrocellulose membrane, etc.) instead of traditional polymer substrates for immunodetection.^49,50 These microfluidic chips still rely on external assistive devices such as various supply pipes and detection modules and are not biochips with data processing and analysis functions in the true sense. Therefore, silicon-based biochips, as shown in Fig. 2(c), have been designed and widely used. They are mainly prepared by micro-nano fabrication technology and integrate a series of actuators and sensors in microfluidic chips.^51,52 The chip usually consists of three parts: silicon wafer-based crystal, microfluidic system, and electrochemical detection system. The electrochemical detection system includes an electrochemical crystal microbalance as well as a signal detection and amplification circuit. These circuits can receive electrochemical signals on the chip surface, convert them into current signals output, connect with the computer, and finally obtain the detection results of the target substance. Silicon-based biochips completely eliminate the limitations of external assistive devices and realize the function of on-chip integrated EI systems. This biochip technology has the advantages of simple operation, high accuracy, multi-channel detection, and strong real-time performance, making it a portable detection platform for EI multi-component detection and widely used.

Based on the principle of specific binding between antigen and antibody, the EI biochip as shown in Fig. 3(a) has the advantages of loose detection conditions, high sensitivity, strong specificity, and fast detection speed. It can be used to detect various biological molecules, such as foodborne pathogens,⁵⁵ disease biomarkers,⁵⁶ toxins,⁵⁷ and hormones,⁵⁸ and has been widely applied in food testing, environmental monitoring, and clinical diagnosis.⁵⁹ There are several ways to achieve multi-component detection, such as using multiple individual detection units for slice assembly, adopting a multi-layer microfluidic chip, or flexibly modifying the chip structure according to actual needs. For instance, Zhao et al.⁶⁰ designed a capture array microfluidic biochip with spacing variations, which utilizes a variable-size capture array and different AD markers captured in different regions to measure the corresponding magnetic bead fluorescence signals. Through a one-time injection, different target concentrations and types of AD markers can be detected. Furthermore, the detection performance of the biochips can be improved by designing appropriate electrode modification methods. For example, Chikkaveeraiah et al.⁶¹ modified an eight-electrode screen-printed carbon electrode array with a dense layer of 5 nm glutathione gold nanoparticles as shown in Fig. 3(b). This design approach provides twice the surface area of normal screen-printed carbon electrodes, thereby providing surface area to attach large surface concentrations of antibody capture. In addition, the arrangement of the counting electrode line and reference electrode line along the entire length of the eight-electrode channel effectively eliminated electrode crosstalk. Moreover, Dulay et al.⁶² functionalized a gold electrode array with internal reference electrode and counter electrode using antibody fragments and placed it in a microfluidic device as shown in Fig. 3(c). The entire setup achieved fully automated EI and had the advantage of saving reagents, in a testing device composed of several reservoirs for analyte and buffer solutions, silica tubing, waste tanks, and fluids controlled by a script-based analysis program.

Biochips have become a hot research topic in recent years due to their advantages, such as high throughput, low cost, and full reaction.⁶³ The structure of biochips plays a crucial role in their performance. First, by integrating multiple sensors and recognition elements on biochips, biochips allow for multiplexed detection of various analytes. Second, as sensors and recognition elements become smaller, the cost of biochips can be greatly reduced. Third, due to the short diffusion distance of analytes, the small size of biochips allows for faster response times, resulting in more complete reactions and increased sensitivity. In addition, because they are usually manufactured using microfabrication techniques,⁶⁴ they are covered with many specific structures or molecules at the micrometer or submicrometer level. These structures can greatly increase the surface area-to-volume ratio of the chip, thereby increasing the contact area between samples and molecules with the chip surface, increasing reaction rates, and giving them huge advantages in high throughput and sensitivity. Overall, biochips provide a more portable and economical way to simultaneously detect multiple analytes and have great potential in various applications, such as diagnosis, environmental monitoring, and drug discovery.^65–67 

Looking to the future, the focus of future research will be to optimize the design, manufacturing, and engineering technology of biosensors to improve their performance and reliability. Overall, biochips, as a new type of high-throughput, low-cost, and portable platform, will greatly promote the development of multi-component detection method. They offer a solution to limitations in existing equipment and analytical instruments and provide new ideas and methods for further research and development in relevant fields. However, the EI biochips may face the following challenges: first, low signal intensity due to the fact that the electrochemical signals are usually weaker than those of optical or fluorescent signals. Second is the low signal-to-noise ratio due to the various sources of noise in electrochemical detection, such as electrode noise or external interferences like electromagnetic noise. Third is the electrochemical stability due to the susceptibility of electrochemical signals to impurities, as well as the decreased electrochemical activity over time. Fourth is the false positives due to the unnecessary cross-reactions between antigens and antibodies. We anticipate that these challenges could be addressed through the utilization of new fluorescent labels or more sensitive electrode materials to enhance signal intensity and stability. A combination with interference filters and noise reduction algorithms is used to improve the signal-to-noise ratio. Search for more stable electrode materials to prevent interference with electrochemical reactions. Reduce cross-reactivity through the use of specific labels and use appropriate probe selection and detection methods.

Furthermore, the integration of AI and biochips has brought new opportunities for the development of biosensors. Ali et al.⁶⁸ reported a disposable fully printed impedance biochips for rapid detection and classification of three bacteria, including Salmonella typhimurium and Escherichia coli. This sensor consisted of a crossed silver electrode coated with silver nanowires. The impedance data of 40 samples for each strain were similar, and a unique sample correlation fingerprint composed of different input features, including power and current–potential (I–V) curve and their first and second derivatives, was extracted from the data and used as features for pattern recognition methods. Algorithms suitable for this process include linear discriminant analysis (LDA), linear maximum likelihood estimation (MLE), and nonlinear backpropagation neural network (BPNN). These unsupervised methods classified the samples with 100% accuracy by a random cross-validation test. Okur et al.⁶⁹ utilized a quartz crystal microbalance (QCM) sensor array-based electronic nose to recognize five pairs of chiral odor molecules, representing ten different volatile organic compounds (VOCs). Given that the enantiomers had their inherent response patterns, the features were processed by ML methods to gain deeper insights into the sensor data and enhance the nose's performance. Using the supervised k-nearest neighbor (KNN) algorithm, the average classification accuracy for the ten enantiomers was 96.1%, indicating the potential for high discrimination accuracy. Beduk et al.⁷⁰ also proposed the combination of sensors with ML for COVID-19 diagnosis. A laser-etching graphene (LSG) device coupled with gold nanoparticles (AuNPs) was developed as an affinity-based biochip platform to detect new COVID-19 variants. A dense neural network (DNN) supervised architecture was used to validate this self-diagnostic setup. Clinical studies were conducted on nasopharyngeal swabs from 63 patients with SARS-CoV-2 variants, non-mutant patients, and negative patients. In particular, these data revealed the ability of the Faradaic electrochemical method to provide chemically diverse signals for ML-assisted high-performance classification. In summary, compared with traditional biochips, AI-assisted biochips improve the performance of biochips through the following points. First, AI algorithms can assist biochips by analyzing large amounts of data, especially multimodal data, to improve the accuracy of biochips. Second, AI algorithms can process large amounts of data quickly, without the need to wait for or undergo complicated processes, thus enabling faster detection. Third, AI-assisted biochips can reduce human errors during the detection process, thereby improving the stability and reliability of detection. Finally, AI-assisted biochips have adaptive capabilities and can dynamically adjust analysis parameters and algorithms to adapt to changes in a complex environment. In summary, AI-assisted biochips have significant advantages in accuracy, speed, stability, and adaptability, which provide broader prospects for their application in medical diagnosis, bioscience research, and other fields. Despite the many strengths of using AI-assisted biochips, there are still some weaknesses and challenges that need to be addressed. The complexity of AI models and the difficulty of data processing may lead to higher computing costs and longer development cycles. Therefore, it is an urgent problem to develop effective strategies to improve the small-scale system of AI-assisted biochips to be more practical and reduce its computational cost and development time.

Although EI biochips can analyze complex components such as genes, proteins, and metabolites, processing and analyzing the associated information of these complex components requires a significant amount of time and manpower. AI can quickly process and analyze information and use appropriate algorithms to analyze data, thereby solving the problem of requiring a lot of time and manpower to process data or complex information. AI can sensitively identify and process multiple specific pieces of information so that the biochips can analyze and respond to potential biomarkers more quickly. In addition, the signal analysis method of EI biochips lacks a unified standard and is influenced by many factors (such as processing methods, microchannels, and experimental environment). AI can propose a universal analysis method that is not only easy to model but also effectively eliminates batch differences in data and decouples multi-component signals.

From the perspective of AI, batch differences in EI reactions refer to the problem of data migration among multiple sample sets. The key is to ensure that the internal distribution and differences of the data sets remain unchanged before and after the migration while minimizing the differences between the data sets. For example, Xu et al.⁷¹ designed an EIS (impedance spectroscopy) biochip that uses ML models to solve batch differences and achieve accurate detection of Escherichia coli. The Faraday impedance system works best when the electrode surface is completely covered by a non-insulating layer. When a microbial biofilm or insulating surface coating blocks the surface, the influence of other impedance parameters becomes significant, i.e., the reduction of the double-layer capacitance (non-Faraday system). Therefore, the accuracy of bacterial detection using only Rct (charge transfer resistance) is affected, resulting in relatively large batch differences. The purpose of integrating ML is to automatically formalize multiple impedance parameters into the recognition machine, which determines the bacterial concentration based on EIS data as shown in Fig. 4(b). Due to its self-learning ability, ML-based EIS biochips should be better adapted to various sensor designs. Wang et al.⁷² proposed a batch effect correction method based on Partial Least Squares Discriminant Analysis (PLSDA-batch) for analyzing microbiome data. PLSDA-batch first estimates and processes latent components of batch variation. Components associated with batch effects were then subtracted from the data to remove batch effects from the microbiome data. Soares et al.⁷³ used interactive document mapping (IDMAP) multidimensional projection analysis of capacitance spectra⁷⁴ to study the selectivity and possible false positives of Staphylococcus aureus detection. Since interference in milk affects the spectra, they used ML techniques of decision tree models to process the data, creating a multidimensional calibration space (MCS) with capacitance values and concentrations as categories. Overestimation and biased performance may be a problem in small datasets,^75,76 which can be avoided by using nested KFold cross-validation and hyperparameter tuning. Bondancia et al.⁷⁷ developed an immunosensor for detecting the cancer biomarker p53 antigen in MCF7 cell lysate using an impedance spectroscopy-based sensor with the decision tree based supervised ML method mentioned above as shown in Fig. 4(b). They created a multidimensional calibration space with 11 dimensions (used for frequency to generate decision tree rules) that have complete selectivity to explain the classification of samples containing p53 from cell lysates and possible interferences.

In summary, AI technology's potent data analysis capabilities render it an effective tool for eliminating batch differences and improving repeatability for EI applications.^78–81 Moreover, it lays a solid foundation for EI multi-component detection and is of great importance for its application prospects.

In general, electrochemical multi-component measurement systems can be classified into two types according to the reaction process: parallel reactions and series reactions. The parallel reaction model assumes that if the reaction processes of reactants O1 and O2 are independent, then the voltammogram of O1 and O2 in the presence of both is the sum of the voltammograms obtained in separate reactions, as shown in Fig. 5(a)-(i). However, in EI, if two current peaks overlap into one peak, although it is theoretically solvable, traditional electrochemical multi-component detection methods are powerless, as shown in Fig. 5(a)-(ii). If the reaction processes of reactants O1 and O2 are in series with the diffusion process, two separated current peaks can be observed on the voltammogram, as shown in Fig. 5(a)-(iii). The concentration analysis of the two reactants is relatively easy. In current electrochemical analysis, in order to eliminate the overlapping of multiple peaks, the standard addition method or the linear extrapolation method is usually employed. But such methods are cost-intensive, operationally complex, and prone to errors.^82,83 It is also possible to change the position of multiple peaks to avoid overlap by adding other reagents or substances,⁸⁴ but this process is time-consuming and not feasible for routine biomarker detection. Therefore, traditional EI methods cannot perform decoupling after multi-component reactions. From the perspective of the ultimate detection goal, it is not necessary to decouple the nonlinearly overlapped curves into two independent curves. Instead, it is sufficient to construct a concentration regression model for each component and obtain the concentration information of each component. For instance, there are many data analysis methods in the ML field that can perform the analysis of overlapped peaks in coupled reaction curves, such as the Fast Fourier Transform,⁸⁵ multivariate calibration methods, partial least squares regression,⁸⁶ and principal component regression.⁸⁷ Zhao et al.⁸⁸ utilized a linear regression model in ML to realize coupled detection of multiple substances, as shown in Fig. 5(b). The data were obtained by cyclic voltammetry in electrochemistry and then decoupled by linear regression model to obtain the prediction results of each component. The method can detect insulin at pmol level and glucose at mmol level in different environments such as alkaline mixed solution, FBS, clinical serum samples, etc. and meets the needs of clinical diagnosis of diabetes. However, when there exists a significant nonlinear relationship between the inputs and multiple outputs of the decoupling model, or when there is inconsistency between local and global structures, and is affected by batch differences, a simple linear regression model is no longer competent. The parameters of the model in ML are learned from the data that depends on the selected pattern, and these parameters are not explicitly fixed in comparison to classical statistical methods. However, there is no universal model in the field of multi-component detection yet. In addition, ML also needs to improve the detection efficiency and detection dimension. Qileng et al.⁸⁹ proposed a ML method based on image matching to construct a wide-specificity immunosensor for detecting various ochratoxins. Based on photoelectrochemical (PEC) signals, fluorescence signals, and colorimetric signals, a broad-specificity immunosensor was constructed to detect ochratoxin A (OTA), ochratoxin B (OTB), and ochratoxin C (OTC). By utilizing a series of chemical reactions, strong fluorescence was generated. Images of the three signals were established and the signals were decoupled through ML to obtain the concentrations of each component.

With the advancement of EI technology, its application in multi-component detection has grown in popularity. However, current EI biochips require tedious calibration and data processing to achieve rapid and accurate detection of multiple components, which limits their practical use. The advancement of AI technology has brought new solutions and methods to this problem. For example, Tang et al.⁹⁰ utilized the DBN-DNN pattern recognition method in conjunction with an array of MEMS gas sensors consisting of eight sensing units based on single walled carbon nanotubes (SWCNTs) to achieve qualitative identification and quantitative analysis of multi-component gases. The accuracy and reliability of their results were higher than those of the traditional BPNN model. Similarly, Sun et al.⁹¹ proposed a deep learning (DL)-based neural separator to address the cross-interference problem caused by the high spectral overlap between CO and CH4 molecules. This successful attempt at applying deep learning methods to tunable diode laser absorption spectroscopy (TDLAS) gas sensors resolved the issue of spectral cross-interference and provided a new direction for multi-component detection. Compared with traditional linear regression methods, AI technology not only has improved accuracy and reliability in EI multi-component detection but it can also considerably improve detection efficiency and the operability of biochips. For example, Xue et al.⁵⁴ designed a functionalized graphene sensing biochip array and developed a powerful bioelectronic sensing platform as shown in Fig. 5(c). The platform consists of more than 200 integrated sensing units, customized high-speed readout electronic devices, and ML inferences and achieved rapid, portable, and reliable measurement. The platform demonstrated a reconfigurable multi-component detection capability, which could provide a highly sensitive, reversible, and real-time response even if there were variations in device performance. They also proposed a calibration method using sensor redundancy and inter-device differences, and a ML model trained with multidimensional information collected through multiplexed sensor arrays to enhance the functionality of the sensing system and the accuracy of ion classification.

In summary, compared with traditional electrochemical multi-component detection, AI-assisted multi-component detection has the following novelty and advantages. First, it has a faster detection speed. Traditional EI multi-component detection requires multiple testing steps, which are cumbersome and time-consuming. In contrast, AI-assisted multi-component detection can quickly identify results of the multiplexed detection through processing and analysis of data. Second, it has higher accuracy and specificity. AI algorithms can identify different types of molecules and specific structures, thereby improving the accuracy and specificity of multiplexed detection. Third, it is automated and has high throughput. AI-assisted multi-component detection can automatically run multiplexed detection, reducing the involvement of operators. The technology can also perform high-throughput detection, improving detection efficiency. Fourth, it is adaptable to complex samples. AI-assisted multi-component detection can process multimodal data, making it suitable for complex samples such as serum, urine, and cerebrospinal fluid. As such, AI-assisted multi-component detection has broad prospects in medical diagnosis and bioscience research.

AI technology has shown great potential for EI multi-component detection. First, Secs. III A and III B of this article have summarized the methods by which ML technology can solve the two main problems of batch differences and multi-component signal decoupling in EI multi-component detection. Specifically, ML technology can model and predict sensor signals using existing data to improve accuracy and stability.⁹² In addition, ML technology can discover correlations hidden in data to better understand sensing signals, thus more effectively decoupling and separating multi-component signals. For example, Lu et al.⁹³ developed a powerful tool for intelligent analysis of nitrochlorobenzene (NA) based on the ANN algorithm as an ML model. Derivatization techniques were used as an auxiliary method for voltammetric treatment to reduce personal errors caused by reading data and to enhance the sensitivity of electrochemical responses at very low concentrations. The authors also used theoretical calculations to compute the adsorption and binding energies and optimize the structures of the prepared sensing materials. Second, we anticipate that deep learning (DL) technology can further improve the accuracy and reliability of EI multi-component detection. DL technology can learn and predict sensor signals by constructing deep neural networks. Unlike traditional ML, DL can mine more information from higher-level data feature learning and use activation functions, convolution, and pooling operations in neural networks to automatically extract features to solve batch difference elimination and multi-component signal decoupling.⁹⁴ For example, the recurrent neural network (RNN) and convolutional neural network (CNN) in deep learning can be applied to deal with the voltage–current response curve of electrochemistry. Among them, RNN can process time series data. For the electrochemical response curve, the current and voltage data at each time point can be used as input for sequence analysis and prediction through the RNN model. CNN can automatically extract the features contained in voltage and current through convolution and pooling operations and perform classification or regression prediction. In addition, domain adaptation technology can solve the problem of batch differences for EI multi-component detection.⁹⁵ Domain adaptation technology can learn the data distribution of EI and “align” between different batches to ensure the consistency and stability of sensor signals.⁹⁶ Domain adaptation technology can effectively eliminate batch differences, improve the accuracy and stability of multi-component detection, and, thus, make EI multi-component detection more practically valuable.⁹⁷ 

Looking to the future, automated data analysis and interpretation could significantly reduce the time and effort for analysis and allow for real-time monitoring of samples. AI's use could permit remote monitoring and data sharing through cloud-based platforms, which could greatly improve accessibility and scalability of EI biochips. Overall, the effective integration of AI with EI biochips holds great potential for advancing multi-component detection in this field. With the continuous development of AI technology, we expect to see the accuracy, speed, and accessibility of EI biochips improve significantly, potentially resulting in significant impacts across a wide range of industries and applications.

Furthermore, we anticipate that, in addition to its application in multi-component detection, AI will drive growth in the following aspects: First, it can be used to predict and maintain the faults of biochips. With the availability of the Internet of Things (IoT) technology, real-time acquisition of sensor data becomes possible. Then, as long as there is enough historical data, ML and DL algorithms can be used to predict part and equipment failures. Second, it can improve sensitivity, repeatability, and accuracy through data-driven approaches. The use of supervised ML models trained on large datasets produced by electrical and electrochemical biosensors/sensors has become an influencing trend in research, and accurate analyses can be performed even in the presence of common problems, such as electrode fouling, poor signal-to-noise ratio, chemical interference, and matrix effect. Third, it can create multidimensional calibration spaces for sensing and biosensing data. The use of calibration spaces should allow the correct classification of unknown samples provided that the data used to generate the rules by ML cover the entire sensing measurement range. Additionally, examination of rules can help in designing optimization performance sensing systems.

However, everything has two sides, and AI is no exception. There are limitations associated with AI such as overfitting, data privacy issues, and the requirement of large datasets to train models.⁹⁸ First, overfitting can lead to decreased accuracy and measurement bias in EI detection. Second, in adversarial environments, some statistical properties of data can be manipulated by capable adversaries when employing ML.⁹⁹ In addition, the incompleteness of the deep neural network training phase makes them vulnerable to adversarial samples, resulting in misclassifications of deep neural networks.¹⁰⁰ Finally, the acquisition of large amounts of data requires extensive support for repeated EI experiments, which would consume significant resources of manpower, time, and materials. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these limitations.

EI has the advantages of high sensitivity and specificity. It has developed rapidly in recent years and has been widely used in the diagnosis of medicine, biochemistry, environment, food, and other fields. However, with the complexity of actual diagnostic needs and the background of interdisciplinary cross-fusion innovation, the cross-fusion and continuous development of EI with biochip and AI technology provides new hope and methods for multi-component detection. We summarized and anticipated the following three points: (1) EI and biochips will be more closely integrated into the field of biological molecular diagnosis in the form of an integrated system, solving the problem of portable platforms for multi-component detection. (2) The close integration of EI and AI will easily solve the problem of batch differences elimination and complex signal decoupling in multi-component detection. (3) In the next few years, we expect that the cross-fusion and innovation of EI, biochip, and AI technology will efficiently achieve significant breakthroughs in key bottleneck problems in multi-component detection. With the continuous development of technology, we foresee that AI-assisted EI biochips have a very broad application prospect in multi-component detection. In the future, we can further tap the potential of AI technology in data processing, model establishment, prediction ability, and other aspects to develop more efficient and accurate detection methods, promote the wide application of EI biochips, and promote the development of life sciences and medical health fields. The deep integration of AI and EI biochips will be one of the main directions in the field of biosensors in the future, which will bring more profound impact and innovation.

This work was supported by grants from the National Key R&D Program of China (No. 2021YFD1800500).

The author has no conflicts to disclose.

Yuliang Zhao: Conceptualization (equal); Formal analysis (equal); Funding acquisition (equal); Methodology (equal); Supervision (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Xiaoai Wang: Conceptualization (equal); Formal analysis (equal); Funding acquisition (equal); Methodology (equal); Supervision (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Tingting Sun: Data curation (lead); Investigation (lead). Peng Shan: Supervision (lead); Writing – review & editing (equal). Zhikun Zhan: Supervision (equal); Validation (equal). Zhongpeng Zhao: Resources (lead). Yongqiang Jiang: Project administration (lead). Mingyue Qu: Funding acquisition (lead). Qingyu Lv: Formal analysis (equal). Ying Wang: Supervision (equal); Writing – review & editing (equal). Peng Liu: Funding acquisition (equal); Supervision (equal). Shaolong Chen: Formal analysis (equal); Writing – review & editing (equal).

The data that support the findings of this study are available from the corresponding authors upon reasonable request.