eChapters

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

Clinical decision support systems assist healthcare professionals in diagnosis, treatment planning and patient management. This chapter introduces intelligent clinical decision support systems and explains how computational intelligence overcomes the limitations of conventional rule-based approaches. It examines artificial neural networks, fuzzy logic, evolutionary algorithms, swarm intelligence and deep-learning models. A computational architecture integrating clinical data, learning algorithms, fuzzy inference and optimization is presented. Applications in heart-disease diagnosis, diabetes-risk prediction and medical-image analysis illustrate the practical value of these systems. The chapter also addresses interpretability, interoperability, data quality, privacy, legal responsibilities, real-time processing and the challenges involved in deploying intelligent decision-support tools in clinical environments.

Biomedical signals provide valuable information about physiological conditions and support continuous health monitoring. This chapter introduces ECG, EEG, EMG, PPG, respiratory and blood-pressure signals and examines the challenges associated with their acquisition and interpretation. It discusses noise removal, filtering, normalization, resampling and artifact correction before presenting time-domain, frequency-domain, time–frequency, wavelet and nonlinear feature-extraction methods. Feature selection and dimensionality reduction are considered in relation to model accuracy and clinical interpretability. The chapter also explores machine learning, deep learning and hybrid approaches through applications involving arrhythmia detection, seizure prediction and wearable cardiovascular monitoring, highlighting their importance in intelligent and personalized healthcare.

Advanced artificial-intelligence models can achieve high predictive performance, but their black-box nature limits clinical trust and accountability. This chapter examines interpretability and explainability as essential requirements for trustworthy healthcare analytics. It reviews intrinsically interpretable models, post-hoc explanation methods, example-based approaches and network-based explanations. A framework combining data preparation, model selection, ensemble learning, calibration, uncertainty estimation and explanation validation is presented. An explainable sepsis-risk prediction case study demonstrates logistic modelling, ensemble methods, Bayesian uncertainty, SHAP attribution and network analysis. The chapter also addresses causal reasoning, adversarial robustness, temporal and multimodal data, human-centred explainability, ethical responsibilities and regulatory requirements for dependable clinical AI.

Symbolic systems provide logical reasoning and explicit knowledge representation, whereas sub-symbolic models offer learning, adaptation and pattern-recognition capabilities. This chapter explains how combining these approaches can create more accurate, adaptive and interpretable intelligent systems. It discusses rule-based systems, logic programming, ontologies, neural networks, fuzzy systems, evolutionary computation and swarm intelligence. Neural-symbolic, neuro-fuzzy, rule-augmented and ontology-aware architectures are examined, along with loose, tight and deep integration strategies. Applications in healthcare, cybersecurity, manufacturing and knowledge-based information systems demonstrate their practical potential. Challenges involving integration complexity, scalability, knowledge engineering, transparency and continuous updating are considered, together with future directions in human-centred hybrid intelligence.

Electronic health records contain extensive clinical, demographic, diagnostic and treatment information that can support medical research and decision-making. This chapter examines the characteristics of healthcare big data and the challenges associated with EHR storage, integration, quality and analysis. It presents an analytics pipeline covering data ingestion, secure storage, preprocessing, integration, pattern discovery, visualization and clinical decision support. Machine learning and deep learning methods are discussed in relation to clinical phenotyping, risk stratification, predictive epidemiology and adverse-outcome prediction. The chapter also addresses data privacy, governance, regulatory compliance, explainability, scalability and bias, highlighting the need for secure and interoperable EHR-mining systems that generate clinically useful knowledge.

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Data-centric artificial intelligence has transformed medical imaging, clinical prediction and electronic health-record analysis, but purely data-driven models face limitations involving generalization, bias, privacy and interpretability. This chapter examines the transition toward knowledge-aware and hybrid healthcare systems that combine machine learning with clinical knowledge, ontologies and knowledge graphs. It discusses explainable AI, clinical reasoning and the integration of structured knowledge into intelligent decision support. Applications include acute-care support, precision medicine, genomic oncology and population-health management. Ethical issues, regulatory compliance, fairness and privacy-preserving technologies are also considered. Future directions include quantum computing, large language models, multimodal integration and equity-focused healthcare-system development.

Healthcare organizations require collaborative learning methods that protect sensitive patient information. This chapter introduces federated learning as a framework for training models across decentralized institutions without transferring raw clinical data. Horizontal, vertical and hybrid federated architectures are examined alongside communication protocols, threat models and security requirements. Privacy-enhancing methods—including differential privacy, secure aggregation, cryptographic techniques and trusted execution environments—are discussed. Applications in medical imaging, electronic health-record analysis, wearable devices and remote patient monitoring demonstrate the framework’s practical value. The chapter also evaluates accuracy, privacy, communication cost, scalability and robustness while addressing data heterogeneity, fairness, explainability, legal compliance and ethical challenges in collaborative healthcare analytics.

Intelligent healthcare is advancing through the integration of multimodal data, trustworthy AI, federated learning, digital twins, IoT devices and real-time decision-support systems. This chapter reviews the evolution of healthcare-data analysis and examines emerging methods for intelligent diagnosis and predictive care. It discusses explainability, privacy-preserving analytics, interoperability, data governance and ethical frameworks necessary for responsible implementation. Wearable technologies and connected sensors support continuous monitoring, while human–AI collaboration improves clinical decision-making. Case studies illustrate practical healthcare applications and implementation strategies. The chapter also considers global-health requirements, particularly in developing countries, and identifies future opportunities for scalable, equitable, secure and clinically effective intelligent healthcare systems.

Computational intelligence is central to the development of proactive, personalized and connected healthcare systems. This chapter examines artificial intelligence, machine learning, expert systems, medical-image analysis, IoT, big-data analytics, cloud computing and blockchain as key enabling technologies. It presents intelligent architectures for diagnosis, monitoring, treatment support and secure health-data management. Privacy, cybersecurity, explainability, bias, fairness, scalability and deployment challenges are discussed in relation to practical healthcare implementation. The chapter proposes a research roadmap involving trustworthy AI, advanced privacy-preserving mechanisms, edge-cloud intelligence, digital twins, multi-omics integration and personalized health models. It also emphasizes accessible computational-intelligence solutions for rural and resource-constrained healthcare environments

The constitution of different solutions plays a significant role in successful demonstration of anatomical techniques especially the special stains. The sincerety begins at the time of fixation of the tissues and deviation at any step may lead to failure of procedure. The chemical reagents required for a particular method have been enlisted at their sites however; the commonly used chemicals are comprehended at one place for convenience of the users.

Teaching and research in anatomy is mainly based on cadaver dissections. A sound knowledge of anatomy is essential from the beginning of veterinary education and the knowledge obtained through dissection of animal body is an indispensable part of the veterinary science.
History of embalming
Embalming is the art and science of preserving the animal by treating with chemicals to forestall decomposition. Embalming destroys the colloid nature of the proteins and neutralizes the active centres of the molecules forming many cross linkages of adjacent protein molecules. Proteins are thus converted to high molecular inert solid materials that can no longer serve as food for bacteria or as a substrate for enzyme action. Embalming preserves the body from decay. The embalmed bodies are stored in tanks containing a fluid composed of formalin, phenol, thymol and rock salt in water in varying proportions.
Claudius Galen (130-200 A.D) did dissections of animals such as the pig or monkey. Leonardo Da Vinci (1452-1519) a pioneer anatomist introduced anatomical drawings. During 15th-16th century the motive was to advance the techniques for the preservation of the dead and to study detailed anatomy by dissections. Andreas Vesalius (1514-1564) believed that dissection was necessary to find out how bodies worked. His lectures were based purely on his observations. He carried out dissection on human cadavers as well as animals to understand Anatomy. As an Anatomist he represents the end of the middle ages obscurantism and the beginning of modern Anatomy and scientific thinking. He is considered the father of modern Anatomy. In 17th century Antonie Van Leeuwenhoek (1632-1723) opened up new horizons

The study of Veterinary Anatomy is an integral part of Veterinary Science as it evolved as a keystone of veterinary education through ages. Osteology (study of bones) is the essential part of the anatomy teaching curriculum. The bones are unsurpassed in the ability to provide the information on the three dimensional form and architecture of the body. It also helps in understanding the disposition and relations of soft tissues along with the course of neurovascular structures in that region. For osteological study, the bones are collected from the carcasses and are duly processed. Bone preparation involves soft tissue removal, cleaning and bleaching of bones and joints, articulation and labeling. The skin, visceral organs and the maximum possible amount of flesh adhering to the bones are removed by knife. Such bones are called wet bones. The whole skeleton is disarticulated; however, the pedal bones are left with the hoof. In case of skull, the mandible is dismembered and the mouth parts are removed. The tongue is carefully sliced to locate the hyoid bone. It is separated at the hyoid process of the petrous temporal bone. The brain is lacerated and goosed out of cranial cavity with a stick through the foramen magnum. The eyeball is carefully extirpated without any injury to the orbit. The nasal cavity is left untouched with the turbinates and cartilages intact. Following steps are involved in processing of bones and preparation of skeleton.
A. Maceration
B. Degreasing
C. Bleaching
D. Varnish
E. Categorization of bones
F. Preparation of articulated skeleton

Animals experience the effects of force in everyday life. For example, the animal body exerts a force on the body and to the objects during various agricultural operations. Furthermore, the gravitational force exerts continuously, for instance while sitting, walking, etc. All parts of the body in one way or the other are loaded by forces. The bones provide rigidity to the body and can sustain high loads. The skin is resistant to force. The cardiovascular system is continuously loaded dynamically owing to the pulsating blood pressure. The bladder is loaded and stretched when it fills up. The intervertebral discs serve as flexible force- transmitting media that give the spine its flexibility. Beside force, animals are using levers all the time in their daily life to increase the ‘force’ that is applied to some object.
Animal soft tissues are complex materials that can exhibit nonlinear, time dependent, inhomogeneous, and anisotropic behaviors. The mechanical responses are reflected in all levels of organism (molecules, organelle and body levels). Several biological activities are under the action of forces, which even govern the gene expression. The study dealing with the forces and their effects on living systems is the Biomechanics.
Biomechanics applies the laws and the techniques of mechanics in the study of biological systems and phenomena. Indeed, mechanics provides the tools for:

Biometry is the measurement of various grossly visible dimensions of organs and their application in statistical analysis to generate biological data.
Various biometrical parameters used for different organs
The biometry of different organs is conducted with the help of non-stretchable thread, Vernier callipers, measuring scale, protector, glass beaker of various sizes, calibrated measuring cylinders, etc. While conducting biometry, it should be kept in mind that the way or the procedure for conducting biometry of different organs varies due to variation in shapes of the same and their positions. Some of the biometrical parameters of different organs and bones are summarized below.
1. Length: May be used to measure the distance between the highest points along the length of any bone or organ. Usually expressed in cm. Non-stretchable thread, Vernier callipers and measuring scale are used for this purpose.
2. Breadth: May be used to measure the distance between the highest points along the breadth of any bone or organ. Usually expressed in cm. Non-stretchable thread, Vernier callipers and measuring scale are used for this purpose.
3. Thickness: May be used to measure the distance between the highest points in regard to the thickness of any bone or organ. Usually expressed in cm. Non-stretchable thread, Vernier callipers and measuring scale are used for this purpose.
4. Volume: It is measures by water displacement of any organ. Usually smaller organs are used. The fresh organ is dipped into a calibrated