Vashisht / Juneja / Kautish Generative AI in Modern Healthcare

1. Auflage 2026
ISBN: 979-8-89881-498-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 300 Seiten

Generative AI in Modern Healthcare brings together research and practical insights on how technologies like machine learning, deep learning, generative AI, and federated learning are transforming healthcare. It explains how these tools are improving diagnosis, treatment planning, and patient care.
The book covers key areas such as personalised medicine, predictive analytics, telemedicine, and AI-based healthcare systems. It also highlights the growing role of AI in medical imaging and diagnostics across fields like radiology, pathology, and cardiology.
In addition, the book explores how AI supports drug discovery, disease prediction, and clinical decision-making, using real-world examples and datasets. It also discusses key challenges, including data quality, bias, privacy, and ethical concerns, as well as secure approaches such as federated learning.
Overall, the book provides a clear understanding of how AI is shaping modern healthcare and improving outcomes.
Key Features
Covers major AI applications in healthcare, including diagnosis and patient care
Explains machine learning, deep learning, and generative AI in simple terms
Includes real-world examples and case studies
Highlights AI use in drug discovery and personalised medicine
Discusses ethics, privacy, and data challenges
Introduces emerging tools like federated learning and large language models
Target Readership
Researchers, academics, students, and professionals in AI, data science, healthcare, and biomedical fields.

Autoren/Hrsg.

Vashisht, Rohit

Juneja, Sapna

Kautish, Sandee

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Leseproben

PREFACE

1. AI in Healthcare: Pioneering Innovations for Improved Patient Care and Future Medical Advancements

Artificial Intelligence (AI) has transformed healthcare by providing novel opportunities to improve patient service, diagnosis, treatment, and administration. This chapter looks at the different aspects of AI being used in healthcare, from improving diagnostic precision through medical imaging and machine learning algorithms to optimizing treatment regimens through personalized medicine and predictive analytics. The fact that artificial intelligence has successfully permeated every other branch of science and technology, as well as numerous other distinct fields, is not new. As the name implies, artificial intelligence refers to the intelligence typically and predictably associated with living things, especially humans, but is recreated in a different form and assigned to computers and/or robots. The fact that AI's applications can be found in the same foundations from which it eventually evolved—the "technology" realm itself—makes its pervasiveness in the postmodern world understandable. According to this claim, every IT industry or online portal uses modern automated customer service chat. AI is currently revolutionizing the global health system, saving lives and enhancing their quality.

2. AI-Driven Healthcare Innovation: The Path Forward Through Smart Medicine

With the availability of Smart Medicine, AI will revolutionize the way healthcare is provided. Such a provision makes the patient treatment much better through advanced diagnostics of the diseases and predictive analysis on the existing disease data. The use of AI algorithms to analyze huge datasets helps in the early detection and treatment of absolute clinical decisions. Various AI-based tools, such as robot-assisted surgery and VHA, have led to substantial progress. Recent studies on AI projects aim to transform the way healthcare facilities are accessible through smart devices, offering real-time monitoring with minimal cost. AI based on data-driven methods proves to be more accurate and clear. The objective of this chapter is to address various aspects of smart medicine being used to solve challenging healthcare issues.

3. New Metrics for Assessing the Effectiveness of Medical Consultation Recommendations

The healthcare sector in today's world is constantly evolving with advancements in technology and the development of sophisticated systems for patient care management. Improving medical consultation recommendations to become effective is another significant area, because new standardized metrics may bring more significant changes than the establishment of traditional metrics. These metrics make it easier for health care practitioners to more precisely assess and improve the quality of patient care since they provide a vivid picture of the effect of a medical advice on the outcomes of patients. More traditionally, metrics in healthcare have often been disjointed, focusing either on short-term clinical outcomes or patient satisfaction. However, the proposed framework is based on integrating those perspectives; it also includes long-term health outcomes, adherence to medical advice, and the optimization of treatment plans based on patient-specific conditions. This framework emphasizes the need for standardization in measurement, which is essential for gathering accurate and useful data. The objective of standardization through metrics is to achieve data that will be standardized and of a type that can be meaningfully compared for the assessment of the delivery of a given healthcare provider and the quality of a consultation. The culture of accountability in practice, combined with an easy-to-use input and analysis system provided for healthcare professionals, is what this system ensures. This, subsequently, will enhance patient satisfaction as well as support better health care outcomes more broadly.

4. DL in Healthcare: From Data to Diagnosis

Management of the healthcare system is a major challenging task for governing authorities. Since the emergence of Artificial Intelligence (AI) in healthcare management, it has driven revolutionary changes that are reflected in improvements across various fields of clinical and biomedical sciences. Deep Learning (DL), a component of AI, has significantly enhanced data management, diagnostics, and therapeutic approaches, positively impacting patients' quality of life, as well as easing the daily lives of clinicians and associated healthcare professionals. This book chapter will provide a comprehensive overview of DL approaches involved in data management for disease diagnosis. In addition, this chapter will elaborate on recent developments in AI-based diagnostic approaches, the strategies involved in personalized medicine, and the support provided to clinicians in selecting therapeutic approaches for specific pathological conditions. Overall, the robust revolution in AI-based clinical approaches can help reduce the morbidity and mortality rates of classified diseases by enhancing the healthcare system. Additionally, it will highlight major challenges, including ethics, legality, bias, privacy, and awareness, in the effective implementation of AI-based technologies within the healthcare system.

5. The Role of Artificial Intelligence in the Healthcare Sector

The integration of Artificial Intelligence (AI) in the healthcare field is changing diagnostics, treatment, and management of hospitals and drugs. In this chapter, the authors introduce AI as a game-changer in the healthcare sector, focusing on its current applications, expected benefits, potential issues that healthcare stakeholders may experience, and the prospective view of AI. Medical equipment, such as MRI and CT scans, as well as genomics diagnosis, has improved through AI, resulting in accurate early disease diagnosis and personalized treatments for every patient. Some of the practical technologies include Butte for Cancer, IBM Watson for Oncology, and AI-driven robotic surgical procedures that make diagnoses and patient relations more accurate. In drug development, AI facilitates the identification of new targets, accelerates the process, optimizes trials, and increases safety. The organizations that implement Artificial Intelligence benefit from effective predictions of various factors in hospital management, as well as increased efficiency in the use of limited resources within the organization. Here, the ethical and regulatory factors related to patient data privacy, data security, bias elimination, and transparency are crucial considerations when integrating AI. These include precision medicine, remote surgery, autonomous surgical robots, and AI, with future technologies such as nanotechnology. Given that the AI poses ethical challenges, these will be addressed while, on the other hand, promoting collaboration will have a significant impact on the delivery of healthcare to patients and providers.

6. Generative AI in Personalized Medicine: Advancing Patient Outcome Prediction

Generative AI applications in personalized medicine have fostered specialized patient outcome prediction. Medical practitioners can utilize highly developed generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), to create complex patient datasets, model potential treatment outcomes, and identify intricate patterns in multi-omics data. Generative AI models are particularly helpful in rare and complex situations where the conventional approach is not fruitful. These models can improve the accuracy of prediction related to disease and treatment effectiveness. The additional features of Generative AI models are designed to protect patient privacy through the creation of synthetic data. Generative AI augments existing data and creates new datasets for patient care outcomes. This chapter focuses on improving accuracy and the use of Generative AI in drug discovery, highlighting the power of Generative AI in clinical decision-making and customized treatment planning. This chapter covers the latest trends, including the integration of Generative AI with electronic health records and real-time monitoring devices. This chapter also covers ethical issues, such as preventing bias and ensuring data security. Generative AI has the power to bring about change in the current healthcare system and provide a more accurate, patient-centric approach to medical outcomes. This chapter is focused on the outcome of the latest technology in the healthcare sector and also covers the current challenges in modern drug discovery.

7. Healing with Data: Generative AI in Medical Diagnosis

Generative AI is a subfield of artificial intelligence that is rapidly transforming the diagnosis of medical conditions, leveraging large amounts of available healthcare data. Conventionally, identifying complicated diseases has required an integration of patient details, medical knowledge, and a thorough review of tests. Nonetheless, generative AI models that can handle large datasets, such as GANs and transformer models, which are utilized in processing and applying layered values to medical imaging, EHRs, and genomic data, provide accurate diagnostic outcomes. This type of AI system enhances early disease diagnosis, providing quicker and more accurate results in areas such as cancer, heart disease, and neurological disorders. In this case, generative AI learns the patterns of data that are most often invisible to the human eye and provides an understanding of anomalies in scans, prognosis, and therapy proposals for illnesses. In addition,...