Keywords

1.1 Introduction

Clark and Lyons in the year 1962 have invented and first introduced us the biosensor for the purpose of analyzing glucose within biological entitles and the way of electrochemical detection of the oxygen. The development of the biosensor is widely noticed in recent past, and such aspects can be noted in the advanced healthcare sensors [1,2,3]. Though, the recent developments of biosensor in the pollutions and environmental systems, agriculture and horticulture, food and allied areas are also important to note. With the biosensor the bio-elements basically communicate by analyzing and being checked and changed into the electrical signal using transducer. In general different types of biosensor can be seen such as resonant mirrors, immune, chemical canaries, optrodes, bio-computers, glucometers and biochips and various biologically connected or derived contents, viz., tissue, cell receptors, antibodies, nucleic acids, enzymes, etc. The growth of biosensor leads to the development of its applications in diverse areas (also refer Fig 1.1) such as:

Fig. 1.1
An illustration of the application areas of biosensors. They include soil, food, water, and environmental quality monitoring, pathogen and drug discovery, toxin, and disease detection.

Some of the potential and emerging application areas of biosensor

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  • In basic healthcare scrutiny

  • In measurement of the metabolites

  • In analyzing and finding sickness

  • Insulin treatment

  • In diagnosis of the disease and psychotherapy

  • In agricultural, veterinary areas

  • In pharma areas like drug improvement including offense detection

  • In ecological management and monitoring

  • In quality control, process control in the industries

  • In the areas of bioterrorism applications

  • In clinical analysis and clinical research

  • In biosensors applications in stretchable electronics

  • In recognition receptors in biosensors

  • In proper biodetection

  • In pharmaceuticals manufacturers and organ replacements.

Biomedical Engineering is a valuable subject in advancement of the biosensor. Nanotechnology and nanofabrication are also important regarding biosensor development [4,5,6].

Therefore biosensor is becoming more and more interdisciplinary in nature. The biosensor substrate holds three electrodes, and these are reference electrode, working electrode and counter electrode. The biosensing now becomes an important technology due to its wider applications such as healthcare, agriculture, food processing and nutrition, environmental monitoring [7, 8], And therefore here IT and allied technologies are widely increasing, viz., cloud computing, big data, smartphone and telecommunications, machine learning, Internet of Things (IoT), and so on. In traditional areas of Biological Science as well biosensor is actively useful, viz., clinical diagnosing, health and clinical monitoring. In preventive health, physiologic functions also biosensor is noticeable and increased its wider scope. Biosensor is constituent with sensor, transducer and associated electrons.

1.2 Objective

The present paper entitled, ‘Aspects of Biosensors with refers to emerging implications of Artificial Intelligence, Big Data, & Analytics: The Changing Healthcare–A General Review’ is theoretical and conceptual work and planned to rich agendas such as:

  • To know about the basics of biosensors such as evolution, nature in brief.

  • To get about the components of biosensors including general applications of biosensors in diverse areas.

  • To learn about the biosensing technologies including application areas of Information Technology.

  • To know about the artificial intelligence, big data analytics, cloud computing including its basic features, characteristics and services.

  • To get the knowledge on applications of the artificial intelligence, big data analytics, cloud computing in brief.

1.3 Methods

This research work is theoretical in nature as depicted its title on ‘Aspects of Biosensors with refers to emerging implications of Artificial Intelligence, Big Data, & Analytics: The Changing Healthcare–A General Review’, and furthermore it is review based. Hence various secondary and primary sources are being used in preparation of this work. For the completion of the work web review and website review of the biotechnology-related company are also studied in order to get data related to rich theme. All gathered knowledge is analyzed and further here is reported in various sections.

1.4 Biosensors: Fundamentals and Types

As discussed before biosensor is having two major components like biological components and physical components. Regarding biological components important are cell, enzyme, whereas amplifier and transducer can be suitable example in physical components. Here it would be worthy to note that, biological components are dedicated in identification and communicate by analyzing and generating signals which are able to be sensed by the transducer. This is properly immobilized over the transducer and useful in remaining and future as long as possible.

At biosensor transducer is responsible for generating signal transformed keen on a detectable response in the biological part [9]. And this is considered as critical component in a biosensing device. Detector is another component in biosensor which amplifies and dedicatedly processes signals by the electronic display system. As biosensors are essential in compliances in various kind of sensitive biological element like tissue, microorganisms, enzymes, antibodies, etc., therefore it is a kind of biomimetic component, and various types of biosensor fulfill each of its categorically requirements [9,10,11].

The rising role and implications of the biosensors in different sectors are just increasing like medicine, healthcare, food processing in international markets. Traditional engineering and sciences principles and methods are widely applicable in better analysis of biological systems, in addition to the materials sciences and nanotechnology. Healthy biosensing practice is required regarding stand-off detection of various components which must be interconnected as depicted in Fig. 1.2.

Fig. 1.2
An illustration depicts biosensor practices. Chemical and biological are connected with radiological and special nuclear which in turn is connected with explosive materials.

Healthy biosensor practices depend on various interconnected facets as depicted

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The technological needs are not always same, and there are always changing applications areas, viz., Biomechanics and allied subjects, ubiquitous devices needed in biodetection, agriculture and production engineering, genetic engineering, food quality and safety. In public healthcare also biosensor applications are rising, and therefore various technologies are important in this regard like artificial intelligence, cloud computing, IoT, etc. Different types of biosensor are briefly mentioned below with their proper classification (also refer Fig. 1.3 in this regard).

Fig. 1.3
An illustration of the biosensor types: 1. Transducing and 2. Biorecognition element. 1: Mass-based, electrochemical, and optical biosensors. 2: Antibody, phage, D N A, enzyme, and biomimetic. Mass bases: magnetic electric and piezo electric.

Some of the major types of biosensor

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1.4.1 Electrochemical Biosensor

This kind of biosensors depends on reaction of the enzymes catalysts which generate electrons or consume as well. Counter, reference and working type are three electrodes considered as substrate of this kind of biosensor. Electrochemical biosensors are basically four types, viz., amperometric, potentiometric, impedimetric, voltammetric.

Amperometric biosensor is a kind of self-contained incorporated devices. This type of biosensors has reaction times, energetic ranges as compared to potentiometric. Potentiometric biosensor offers logarithmic reply by means of a high energetic range. This biosensor basically measures electrical potential of an electrode at the moment when there is no current present. It depends on three additional types of sensors. As this is a kind of potential chemical sensor therefore here concentration of the analyte in the gas or solution phase is considered as important and valuable. Impedimetric biosensor is sensitive indicator with different chemical and physical properties. This type of biosensor used is widely increasing throughout. The techniques and methods of this type of biosensor are also executive in other areas and to examine catalyzed responses of enzymes, etc. In other words, impedimetric biosensor is a sensitive technique which is required for the analysis of the interfacial properties related to biorecognition events. Voltammetric biosensor is basically built with a carbon glue which is additionally adapted with hemoglobin including four prostatic groups of them, i.e., voltammetric biosensors basically detect analyte by determining the amount of changes in the current as a function of applied potential [12, 13].

1.4.2 Physical Biosensor

Physical biosensor is another major type of biosensor; this is considered as most basic and widely used sensor. This categorization is basically happened regarding the inspection of the human minds. The physical biosensors are classified into two types, namely piezoelectric biosensors and thermometric biosensors. Physical can be able to sense a biological event with the changes in physical phenomena, and among these important are mass, resonance frequency, refractive index, fluorescence, etc. There are two types of physical biosensor, viz., piezoelectric biosensor and thermometric biosensor.

Piezoelectric biosensor is a collection of analytical devices based on the principle of affinity interaction recording. In other words, this kind of biosensor depends on the law of ‘affinity interaction recording’. Here principle of oscillations is considered as worthy and important for the purpose of mass bound on the piezoelectric crystal surface. Piezoelectric biosensor is a kind of devices that are useful in managing and effecting piezoelectric effects to compute and measuring changes in the following, viz.:

  • Pressure

  • Temperature

  • Force

  • Strain, etc.

Piezoelectric biosensor is with the modified surface with the antigen, antibody, etc. The piezoelectric material lies on three main operational modes, viz., transverse, longitudinal, shear. Several biological reactions are associated with the release of heat. Thermometric biosensors measure the temperature change of the solution, and there are many biological reactions connected with invention of heat, and this refers to the thermometric biosensors [9, 14]. This is used in serum cholesterol.

1.4.3 Optical Biosensor

This kind of biosensor is being used with the optical measurement principle. Here fiber optics and optoelectric transducers are being used [9, 15, 16]. Optrode combines with optical and electrode to find out and involving with antibodies and enzymes. This kind of biosensor has increased its uses in different areas; it is due to its ample benefits such as higher speed, sensitivity and robustness. Moreover optical biosensors are small in size and very much cost-effective over the traditional systems. According to market expert optical biosensors are useful due to its scope in direct, real-time and label-free detection of many chemical substances and biological substances. Direct optical detection biosensors and labeled optical detection biosensors—these two are major types of optical biosensor.

These are some of the important types of biosensor as mentioned above. However based on different criteria and features biosensor can be additionally with few more variants and some of them are mentioned below.

Wearable biosensors: this is a kind of electronic device used to wear, i.e., human body by various means such as:

  • Smartwatches

  • Smart T-shirt or shirt

  • Tattoos

  • Shoes

  • Thumb, etc.

Such types of wearable biosensor are responsible in getting blood glucose, blood pressure, rate of the heartbeat, and so on. Some of the wearable biosensors are depicted here in Fig. 1.4.

Fig. 1.4
An illustration of wearable biosensors. It includes glasses, mouth guard, clothing, strip, belt, headset, armband, watch, ring, and shoe pointed at a human mannequin.

Major examples of wearable biosensors

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Enzyme biosensor is a kind of biosensor which is having the nature of analytical device which is basically used in merging of the enzymes by the use of a transducer [17, 18]. It is worthy to note that enzyme-based chemical biosensors are basically based on biological recognition.

DNA biosensor is an important kind of biosensor which can be identified based on nucleic acid for the purpose of analyzing simple, rapid and economical testing. DNA biosensor is worthy and important in the food analysis, clinical research including environmental protection, etc. However it is worthy to note that for better and efficient detection SAM and SELEX technologies are basically worthy and important. With DNA biosensor accurate and required data in a simpler, cheaper and faster manner can be possible to retrieve.

1.5 Biosensors and Information Technological Involvement

Biosensor is increasing with the Information Technology applications and integrations. As Information Technology is dedicated in different information activities such as collection, selection, organization, processing, management and dissemination, therefore various subsections and parts such as Software Technology, Web Technology, Networking Technology, Multimedia Technology, Database Technology, etc. involve into the biosensor-related activities. Here among the subfields of IT and Computing Computational techniques, Networking Technologies and Communications played valuable role. Among the types of biosensor optical biosensor is important one and use of Communication Technology is prime example in this regard [12, 19].

Optical sensors are growing rapidly over the years and here uses of optical sensing other than transduction methods are widely useful. In optical biosensor one of the important features is integration of the high sensitivity of fluorescence detection alongside with the high selectivity powered with ligand-binding proteins. The field image processing is dedicated in optimizing the medical fields with utilizations of the computer vision, virtual reality and robotics, and so on. There are certain areas where IT are effectively used as depicted in Fig. 1.5.

Fig. 1.5
An illustration of the areas where I T is used is as follows. 3-D imaging interaction of various components, live cell fluorescent, Theranostics, and implantable biosensors, novel biosensors regarding live cell imaging, and image analysis.

Effective and potential areas of IT in biosensor and healthcare

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In the Information Technology segment there are many emerging and potential areas and among this important are Virtualization Cloud and Fog Computing, Data Analysis and Analytics and Big Data Management, Internet and Web of Things, Wireless Networks and Edge Computing, Converged Networking, HCI and Usability Systems, etc. The applications of the Cloud Computing and Internet of Things alongside Artificial Intelligence, Robotics are also rising in the areas, viz., biosensor and medical systems and technologies [20, 21]. In the next sections different aspects and applications of artificial intelligence, big data analytics, Internet of Things first elaborated at first and then basic, emerging and potential applications of these technologies and systems into the biosensors and allied systems.

1.6 Artificial Intelligence Basics with Biosensors and Smart Biodevices

Arthur Samuel coined the term in the year 1959 first having deep involvement and exploring the study and construction of algorithms; and gradually it has come to today’s shape. Artificial intelligence is a system, procedure and mechanism regarding the designing and developing intelligent products, services which are able in human-like performance, activities, mimic their action, etc. It lies on problem-solving and decision-making process. Here rationalization and actions are possible based on case and context. Due to wider applicability and importance of artificial intelligence in different areas, different sectors and industries various educational institutes, research organizations are putting their importance in research and practices in artificial intelligence. Artificial intelligence may be tested and used in the areas like healthcare and medicine, governance and administration, business houses politics and bureaucrat, transport and tourism, education and research, etc. [9, 22]. There are rises of AI-based systems and tools such as automated machines, intelligent devices and products, self-driving cars. In the industries like finance, banking also artificial intelligence applications have increased in recent past and drastically. The latest features of the artificial intelligence include:

  • Artificial intelligence lies on simulation and also human intelligence systems which are required in devices and machines.

  • AI is dependent on learning, reasoning in order to fulfill the aim and objective.

  • Weak artificial intelligence and strong artificial intelligence are two different types of AI which are able to manage and effect more complex and human-like [23,24,25].

There are different type of approaches of AI, viz., supervised, unsupervised and reinforcement as depicted in Fig. 1.6. Furthermore, artificial intelligence is two types and here weak artificial intelligence is important in designing of the simple system that can do a particular job, whereas strong artificial intelligence is more advanced which acts like human and able to perform jobs with complex and complicated system. Many artificial intelligence jobs can be done or possible to solve problem without having a person. Here machine learning is a part or subset of the artificial intelligence within Computer Science. Some of the statistical techniques and mathematics are important in cutting-edge AI practice with the abilities in computation, decision-making, proper data management [21, 26, 27]. Some of the approaches of machine learning are depicted as under.

Fig. 1.6
An illustration of three types of learning includes supervised: classification, unsupervised: dimensionality reduction, and reinforcement: gaming, finance sector, manufacturing, inventory management, and robot navigation.

Various approaches in AI with some other facets

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  • Decision tree learning (DTL)

  • Association tree learning (ATL)

  • Artificial neural networks (ANN)

  • Deep learning (DL)

  • Inductive logic programming (IPL)

  • Clustering.

Healthy precision medicine treatment regarding individual patients becomes possible with AI and wearable sensor-dependent mechanism systems. Better and healthy integration of these systems is dedicated and brings better, efficient and healthy patient data management systems [8, 28]. Such sensors are able to collect fast and reliable data and help in health, fitness and allied aspects.

Allied and some other emerging technologies like Internet of Things (IoT), big data, AI-based biosensors are offering new opportunities, and at the same time it occurs few challenges too. Artificial intelligence is being used in analyzing the raw signal form a biosensor in by different means such as:

1.6.1 In Categorization

Artificial intelligence applications are able to increase the specificity of the sensor, and here the appropriate algorithms are useful in sorting the signal into different categories, and thus directly and indirectly it helps in healthy and sophisticated biosensor practices; it includes the wearable sensors.

1.6.2 In Detecting and Finding Anomalies

Sometime there are chances in error in signaling in the sensors, and here artificial intelligence can be effectively used in detecting anomalies. It can be directly expressed that a particular sensor is right or wrong. The advantages of artificial intelligence in biosensors help in healthy and effective biosensor practices.

1.6.3 In Reducing Noise on the Sensor Systems

Noise of the sensor is common; however artificial intelligence is perfectly suitable in reducing noise on the sensor while transferring data. Biosensor therefore required the support of the AI including other elements such as ML, DL and emerging robotics [9, 19].

1.6.4 In Identification of the Patterns

Identification of the patterns of different types on sensor data can be identified and split with the help of AI-based systems. AI can be trained to do pattern recognition. The healthcare organizations, AI organizations are enhancing AI-based systems, and this scenario is rising [29, 30].

Therefore the AI and computational intelligence are being used in biosensor. Among different kinds of biomedical devices biosensors are widely increasing due to its benefit in healthcare monitoring and data collections. Here additionally Internet of Things, machine learning algorithms, cloud computing are being used in making of efficient and functional devices. For example in cardiac biosensor data are basically gathered based on heart symptoms, and it is useful in cardiovascular diseases. Cardian biosensors are able to gather different status and features of the cardiovascular diseases by analyzing status of the myoglobin, cardiac troponin, interleukins and interferons. With the biosensor irregular electrocardiogram patterns, rates of the respiratory, blood pressure, saturation of the blood oxygen, temperature of the body can be analyzed using AI-dependent biosensors. Additionally machine learning allows novel modeling and predictive methods in some of the following abilities, viz.:

  • In identification of finding particular response

  • In ability regarding finding delicate changes

  • In proper and structural stability including functional stability

  • In monitoring various health issues and systems

  • In proper and sophisticated wireless transmission of the current and real-time data (Fig. 1.7).

    Fig. 1.7
    In a schematic diagram Portable devices point to machine learning. Wearable devices point to deep learning. They point to diagnosis, treatment, monitoring, and food safety. This results in an improved healthcare system.

    AI and its subfields influences in biosensors in improving healthcare systems

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1.7 Big Data Analytics, Allied Technologies and Biosensing

In the year 1990 the term big data was first coined and due to its role and importance gradually becomes an established name. The massive growth of data in various areas and domains leads more progress in data management using big data management tools and techniques [9, 31, 32]. The low-cost internet service providers created the craze in internet uses, and this results in huge data generation. However it is important to note that before coined the term ‘big data’ in the year 1984 Tera Data Corporation first initiated and gives the concepts of big data or massive data with the ‘parallel processing DBC 1012’. Soon after this various types of data, i.e., structured and unstructured management concepts and techniques were gradually improved and come to today’s shape and periphery. In the year 2001 Seisent IMC also performed a perfect role for the development of the big data management systems. Google, Apache, MapReduce techniques (by Google) and Hadoop (by Apache) are important name in initial development of the big data management. Various other big data techniques and companies such as Oracle, EMC, Dell, IBM also take part in the proper big data management movement. Initially the concept lies on mining of the data with a particular timeframe and here database and data warehouse also allied and related. Here for managing and reducing the complexity of data various kind of tools and technique are used. Data become important in almost all the places and sectors, and as a result many other nomenclatures are being developed, viz., analytics, business analytics, data analytics, data science, etc. Various applications in other sectors lead other subjects and domains.

  • Therefore, big data is concentrated on data that is huge in size. It is used to describe a collection of data huge in size and complex.

  • Stock exchange, social media, jet engines, etc. are prime example in big data management.

  • Big data therefore can be structured, unstructured and semi-structured in nature.

  • Big data comes with the features of volume, variety, velocity and variability, and with this it offers the efficient customer service, operational efficiency, etc.

There are many applications of the big data and analytics in the fields of biosensors. As biosensor is dedicated in collecting various data throughout its operation, therefore the growth of the data and analytics can be seen all over. The AI algorithm-integrated data analytical system is important in collecting and providing accurate data and therefore important in managing different abnormalities in healthcare. AI-based data analytical tools are responsible to extract various information from various database and may be suitable in offering various data as per requirement such as data related to the heartbeats. As an example we can note long-term ST database which is able to collect data through the ECGs.

In collecting appropriate and proper data various methods of the artificial intelligence are being used such as supervised learning, unsupervised learning, reinforced learning. The data prediction in many ways depends on such methods [33, 34]. Since there are variety of the data already learned about among them wearable biosensor considered as important one which is very much sophisticated in regard to the monitoring health via proper analysis of the biological data like biofluids (e.g., saliva, sweat and blood). And such prediction is worthy to the physician and heath expert regarding further decision in patient monitoring and management. With big data-enabled biosensors following become easy and efficient such as:

  • In managing proper medical imaging and DSS development.

  • In proper and efficient real-time alerts.

  • In healthy monitoring and optimization of healthcare systems.

  • In better and preventative medicine analysis.

It is important to note that big data and analytics technologies required in knowledge discovery, problem-solving, data processing, analytical modeling which are worthy and important in healthcare, patient management and treatment. The big data analytical framework in connection with other technologies helps in remote monitoring of healthcare including physical daily activities of healthy as well as unhealthy population.

However, there are different technologies from the IT and computing also dedicated and required in biosensor and allied technologies and among them important are Internet of Things or IoT. Internet of Things lies on various objects, and it is able to collect required and timely data. The entrepreneur Kevin Asthton coined the term in the year 1990 and gradually improved various concepts, thoughts, techniques and strategies, etc. Different kinds of built-in sensor are integrated in the objects or system in order to collect various data. Some of the latest features are powered by the auto-adjustment like heating, lighting, etc. for building smarter life and technology enhanced society. Internet of Things plays an important role. The integration of the AI and ML in IoT devices is also valuable in order to do intelligent services and feedback systems. Here at Internet of Things IP address, wireless internet, embedded sensors are applicable to enhance the service and enhance professional and personal activities [34, 35].

The growth of the Internet of Things in other sector and areas leads other branches of IoT, viz., Industrial Internet of Things (IIoT), Agricultural Internet of Things (AIoT), Internet of Medical Things (IoMT), etc. as far as Internet of Medical Things or its medical applications are concerned; it is a worthy tool in collecting important health-related data. In some of the wearable devices used in healthcare Internet of Things is important for collecting various data in different formats in order to patient management.

These days developed societies are being connected with the advanced and intelligent healthcare network and here biosensor plays a critical role. Here in processing of advanced features of biosensor it lies on Internet of Things, big data analytics-integrated systems and obviously backed by the artificial intelligence. In recent past healthcare units and organizations are facing lot of challenges in data collection and generation and IoT, and some allied technologies come with the new hope of wellness. The growing healthcare data help in improvement in Electronic Heathcare Records, Mobile Healthcare, Telemedicines, etc. Remote monitoring systems using IoT are an important step in advancing Healthcare Informatics powered by the intelligent algorithms, tools and techniques with faster analysis and expert intervention for better treatment recommendations. The IoT-based sensor is responsible in collecting continuous data using proper signaling systems. Here signals are stored and thereafter properly stored in analytical techniques and systems using machine learning algorithms. Another important technology is cloud computing which helps in proving healthcare and medical systems like:

  • Improving and advancing telemedicine systems powered by clouds

  • Sorting and easy accessibility in medical imaging systems

  • Proper and remote public healthcare systems

  • Advanced patient self-management

  • General healthcare operations and hospital management

  • In allied healthcare and therapy management, etc.

Therefore artificial intelligence, robotics, Internet of Things, cloud computing and some other technologies are important in enhancing healthcare systems with due implications in the biosensors such as proper and efficient patient monitoring and patient management, cell-based biosensor activities, biosensor analysis and also imaging which are antibody based. As far as detecting a symptoms also this is considered as important. In cardiovascular areas too cloud big data-based systems are worthy similar to transduction technology. Biosensors are applicable in diabetes applications, and here directly and indirectly cloud computing, artificial intelligence and similar technological usages are important and valuable.

1.8 Biosensor, Futuristic Healthcare: Some Important Perspective

The advancement of the Information Technology leads various newer services in healthcare systems. As far as biosensor is concerned big data and allied technologies even also able in some other operations and dimensions such as X-rays, MRIs, ultrasounds and such generated various data through the images and to identification of such abnormalities here big data analytics including AI are important. Here cloud can be considered as important in storing and records management for current and future utilizations [9, 34]. Here machine learning can be considered as important in predicting various diseases. Analyses of the vast and large datasets are powered by the medical imagers using data analytics systems. In pharmaceutical industry also biosensors are useful, and here some of the analytical techniques are applicable and employed. It is a fact that machine can be used based on need and requirement; however it could not be considered as alternative. Though in pharma and healthcare industry biosensor can be useful in increasing the diagnostic speed and also helps in healthcare systems in enhancing patient care and communication.

Due to the advancement of the technology it is now creating new potentiality and possibility in advancing automated drug delivery, data and sensor-driven applications. The mobile-based applications are rising day by day for collecting data and patient management. Here business analytics are useful in analyzing and collecting data related to the patients. The biosensor is able to predict drug requirement based on AI-dependent prediction systems here body-worm sensor. Diet-tracking applications are also able to develop real-time data collections. Smart diagnostic and therapeutic devices cutting-edge engineering in the biosensors can lead more sophisticated healthcare systems. Here healthcare organizations can also be involved in advancing the systems of healthcare with advancing biosensors [22]. Some of the interconnected advanced systems and technologies can improve biosensors uses, and these are:

  • Cloud computing and big data

  • Artificial intelligence with machine learning

  • Deep learning and robotics

  • HCI with usability systems, etc.

This technology is important in developing effective and low-cost biosensors and processing systems as well. In prediction analysis robotics and AI technologies, etc. are important in connection with the data analytics systems. Gradually the healthcare organization becomes able to offer cheaper health services, and in many context it is due to biosensors. Internationally biosensor industries have been increased, it was 25 billion USD market, and it is expected to grow 7.4 percentage in 2027. Demand for biosensors is increasing owing to wide use in clinical care and advancement in the areas such as diagnosis, patient health monitoring, detection of disease and human health and patient management with proper robust growth opportunities in the future. According to the Global Market Insights the biosensor market grows with following:

  • Non-wearable biosensor segment market share is expected to rise at 37%

  • Electrochemical biosensor segment is expected to grow 7.5%

  • Home healthcare diagnostics is expected to grow 23%.

Furthermore as on 2020 North America market value is 10 BN USD, whereas European market is 8 BN USD and expected to grow in different categories and advances in biosensors [27, 35].

1.9 Conclusion

Biosensor including thermal, electrochemical, piezoelectric, optical—all types are increasing all over due to wide potentiality in different segments. Electrochemical biosensors are rising gradually due to wide scope and market. The needs of embedded systems have risen in recent past, and therefore healthcare organizations are also putting importance on biosensor. The advancement of Information Science and Technology leads to various kinds of benefits on society and human being, and healthcare is one of the latest. Lives become easy, effective and advanced with health informatics practice, and here emerging technologies are important in developing human lifestyle and comfort. Due to the implications of the biosensor in environmental monitoring, biological defense and food and nutrition, etc. the allied technologies of IT and computing play a leading role. Here artificial intelligence, machine learning, deep learning play a great role in modernization of the biosensors and allied activities. As biosensor is required in overall healthcare improvement therefore organizations, institutions and educational, research centers need proper step in developing manpower in the field of healthcare informatics.