Innovation in Healthcare Technology and Management

Mobile health (mHealth) has profoundly transformed healthcare delivery by harnessing portable devices and digital applications to extend access, enhance patient engagement, and streamline health management processes. This chapter critically reviews the evolution and current state of mHealth, tracing its progression from basic SMS-based interventions to sophisticated smartphone applications, wearables, and AIdriven tools. Emphasis is placed on how mobile technology has addressed—though not fully eliminated—systemic inequities in healthcare access, notably by reducing geographic barriers and offering remote consultations through telemedicine platforms. Yet, persistent challenges remain, including the digital divide, issues of data reliability, regulatory complexity, and vulnerabilities in privacy and security, all of which constrain equitable benefits across populations.

By examining major domains of application—telemedicine, chronic disease management, health monitoring, and mental health support—the chapter synthesizes evidence of outcome improvements, particularly in underserved settings. Case studies demonstrate tangible benefits such as improved chronic disease control and early detection, but also reveal lessons on low adoption rates among marginalized groups. The critical analysis highlights that while mHealth solutions have expanded access and engagement, their effectiveness depends on robust implementation strategies for inclusivity, data integrity, and cultural adaptation. The reference list follows standardized citation formatting throughout for consistency.

Key Contributions:
- Synthesis of mHealth’s evolution and current technological landscape.
- Evaluation of mHealth's effectiveness in reducing healthcare inequities and barriers across diverse populations.
- Critical appraisal of challenges—digital divide, privacy, regulatory compliance—and their impact on real-world equity.
- Recommendations for improving inclusivity, adoption, and data reliability in future mHealth innovations.

The chapter probes how Artificial Intelligence (AI) is altering existing medicine. An essential part of treatment is design, empowering precision medicine, and using robotic technologies to enhance medical processes. AI also improves patient care by enabling remote nursing, managing long-term infections, and encouraging psychological wellness via virtual aides. Despite these benefits, bias, statistics secrecy, and the requirement for open and transparent monitoring outlines are some of the ethical and permissible concerns that arise when AI is used in medicine. The assistance of AI adoption for patients and the pharmaceutical trade is twofold: first, it has reduced the time and cost related to medication development. In general, a medication used in medical exercise has gone through manifold phases, including safety validation, response, screening, and detection. AI systems could help estimate three-dimensional structures and protein utility, which could support recognizing potential action targets.

Medication delivery, absorption, biotransformation, and excretion are important pharmacokinetic aspects that disturb how they work in the body. Consequently, researchers tried to apply AI measures to the day-to-day actions of medical chemists. The research on AI-based methodologies executed with 84% to 87% accuracy, which shows AI could help in pharmacokinetic technique assessment. AI has the potential to enable effective drug design and its synthesis. AI in medication research is in an early stage, with more significant complementary development, and ethical concerns are required for a well-organized and modified health organization. This chapter provides a comprehensive overview of AI’s existing uses and forthcoming prospects in medicine.

Wearable technologies and smart medical devices are transforming healthcare by enabling users to monitor vital signs continuously and in real time, thus supporting more individualized and data-driven medical care. By examining core components such as biosensors, microcontrollers, and wireless communication systems, this chapter explores wearable technologies within the context of the Internet of Things (IoT) and data analytics. It further investigates the applications of wearables in healthcare, including remote monitoring, rehabilitation, disease prevention, and elderly care. Key challenges such as battery limitations, sensor accuracy, and the ethical and legal concerns surrounding these technologies are also discussed. Additionally, the chapter highlights the integration of Artificial Intelligence (AI) and Machine Learning (ML) in wearable systems, the evolution of next-generation devices, and their implications for public health, especially in epidemic preparedness. Finally, it identifies future research directions that encourage multidisciplinary collaboration and the development of patient-centered healthcare. As wearable technology continues to evolve, it holds significant potential to improve patient outcomes and advance community health.

 Blockchain adoption in healthcare is changing the way in which medical information is managed, shared, and secured. As a distributed digital ledger, it helps solve big problems in healthcare, such as scattered data, privacy risks, and slow or inefficient exchange of records. With the help of cryptography and consensus mechanisms, blockchain ensures that records are permanent, transparent, and easy to track. Its applications are wide-ranging, covering Electronic Health Records (EHRs), drug supply chain tracking, insurance claims management, and protecting the accuracy of clinical trial data. Smart contracts can also automate routine tasks, reduce fraud, and cut down operating costs. A key advantage of blockchain is its decentralized design, which gives patients more control and access to their health information, supporting a patient-centered approach to care. However, there are still challenges, such as limited scalability. This chapter reviews current blockchain applications in healthcare, explains its transformative role in improving healthcare delivery, and discusses the technical, ethical, and legal barriers that must be addressed for wider adoption. 

The advent of robotics is changing the world economy. The World Health Organization (WHO) reports that more than 1 billion people live with some form of disability. In addition, changes around the world mean that patients are getting older and sicker, with many people living alone at home or in hospitals and relying on some form of aid to meet their specific needs. On the other hand, research and innovation in medical robotics have increased tremendously in recent years. Medical automation and robotics have the potential to deliver improved patient care, innovation, and safety for patients and physicians. This paper presents a systematic mapping study to understand the emerging role and the application of robotics in healthcare, and identifies existing and likely future challenges to maximize the benefits associated with robotics and related convergent technologies. This study employs a qualitative research technique. The methodology includes a comprehensive literature review on robotics in healthcare automation to gain qualitative insights. The research revealed significant improvements in healthcare efficiency and decision-making accuracy due to automation and robotics. The findings of this study offered valuable insights into the benefits and challenges of automation and robotics in healthcare.

Technological advancements have paved the way for revolutionary transformations in the health care sector. A significant shift in patient care has been brought about by the development of technologies such as wearables, the Internet of Things, artificial intelligence, and the metaverse, which have made possible features like telemedicine and remote health monitoring. Incorporation of these technologies has resulted in more efficient and effective diagnosis, treatment, and nursing care. The digitization of health care data has paved the way for the easy handling and sharing of medical records. Meanwhile, there are various challenges specific to sensitive patient data involving security, privacy, and interoperability. Blockchain technology, with its unique attributes like immutability, decentralization, and security, became a key priority in developing applications in different sectors, ranging from financial to education. Blockchain's privacy feature has recently prompted its integration with numerous healthcare applications, reflecting the critical necessity to protect patients’ private information. The cryptographic concepts in blockchain allow the encryption of medical data, prevent unauthorized access, and enable tamper-proof storage. This work focuses on the application of blockchain in various sectors of healthcare and examines how the integration of this technology has benefited those applications.

The health care sector has seen an evolution due to rapid advancements in wearable technology and smart medical devices, which have fundamentally transformed patient monitoring, diagnosis, and treatment. This chapter focuses on how these technologies are emerging and how they might enhance personalized therapy, remote patient management, and preventive healthcare. This chapter covers a range of wearable devices, such as fitness trackers, smartwatches, and medical-grade sensors, and assesses how well they can continually monitor vital signs and gather data in realtime. Future developments in this rapidly growing field are also explored, along with related concerns including user adoption, data security, and regulatory challenges. This chapter aims to offer an in-depth primer on wearable technology, smart medical devices, and their revolutionary role in current healthcare delivery.

The purpose of the present study is to comprehend the significance of artificial intelligence adoption in healthcare services and identify the drivers that are critical in investigating healthcare professionalsƒ?(tm) intention to adopt artificial intelligence in healthcare services. A comprehensive review of the literature and expert opinion was carried out to identify the critical drivers to the adoption of artificial intelligence in healthcare services. A total of five critical drivers for adopting artificial intelligence relevant in the context of healthcare services have been proposed. The proposed critical drivers were prioritized using a multi-criterion decision-making (MCDM) technique known as the fuzzy Analytic Hierarchy Process (Fuzzy AHP). This study is among the pioneering studies in exploring and prioritizing the critical drivers influencing artificial intelligence adoption in healthcare services. The findings of the present study will be fruitful for both the industry and academics. It will apprise artificial intelligence-based healthcare services providers about the critical drivers that need to be prioritized to boost user adoption. 

In today’s rapidly evolving healthcare landscape, collaboration across various disciplines has become essential to addressing complex health challenges. This chapter explores the importance of a multidisciplinary approach in healthcare, where the combined expertise of medical professionals, engineers, policymakers, and others leads to innovative solutions. It begins with a historical perspective, highlighting the evolution of healthcare through multidisciplinary collaboration and showcasing key milestones such as the integration of technology in surgery and the synergistic efforts of public health and epidemiology.

Key areas of multidisciplinary advances are examined, including biomedical engineering’s role in diagnostics and treatments, and the impact of artificial intelligence in clinical decision-making. The chapter also delves into the pharmaceutical industry’s collaboration with life sciences to advance personalized medicine, as well as the contributions of public health professionals in shaping global health policies. Special focus is placed on dentistry, showing how oral health is connected to overall well-being and its importance in multidisciplinary care teams.

The chapter emphasizes the role of interdisciplinary research and education in training future healthcare professionals, highlighting case studies of institutions fostering crossdisciplinary learning. Technological innovations such as telemedicine, robotics, and genomic medicine are explored as key drivers of multidisciplinary care, offering transformative possibilities in patient treatment and health monitoring. It concludes by addressing challenges such as communication barriers and ethical dilemmas in multidisciplinary teams, while envisioning future trends like precision medicine and wearable health technology that will shape global health initiatives and foster deeper collaboration across healthcare disciplines.

The mobile technology landscape has dramatically evolved from simple communication devices to highly sophisticated tools that serve critical functions across all aspects of daily life, especially in the health sector. Initially limited to texting and calling, mobile phones have transformed into powerful computing platforms with the advent of smartphones and advancements in wireless technology. These devices now support a wide range of applications capable of executing complex tasks, including health management. Among the most significant developments in mobile technology is the emergence of health applications. These advanced apps deliver healthcare solutions by leveraging smartphone features such as sensors, GPS, and internet connectivity. Mobile health applications have revolutionized healthcare delivery—from tracking calories and exercise routines to managing chronic conditions and enabling remote consultations. With ongoing advancements in mobile technology and the integration of cloud computing, big data analytics, and artificial intelligence, health applications are poised to become an integral component of the modern healthcare system.

Numerous women's health apps worldwide monitor menstrual cycles and forecast related outcomes. However, the majority of these apps focus primarily on symptom tracking and cycle prediction. There are relatively few solutions designed to enhance women's physical health and immunity during menstruation. Additionally, there is a lack of comprehensive frameworks offering personalized exercise and dietary recommendations tailored to the user's menstrual phase. Even fewer systems provide individualized lifestyle guidance and raise awareness about menstrual health by leveraging Generative AI. To address these gaps, we have developed a Phase-Based Women's Health Recommendation and Prediction System that leverages optimization algorithms to deliver personalized exercise, dietary, and lifestyle recommendations based on the user's current menstrual phase. The system integrates a prediction model to forecast both the menstrual cycle date and phase, along with a large language model to raise awareness by answering common user queries, empowering women to improve their overall health. The predictive model predicted the length of menses with 97% accuracy. The system emphasizes the importance of integrating menstrual phase-based health recommendations to foster improved cycle health and well-being.