Multimodal AI in Healthcare

This book introduces readers to the practical use of multimodal AI in medicine, emphasizing how the integration of diverse data transforms diagnosis, prognosis, and personalized care across clinical domains—going far beyond a focus on algorithms alone.

The book begins by introducing the shift toward integrated AI in healthcare, explaining multimodal data and its workings. It explores radiology-pathology synergy, precision care through genetics and history, and cross-specialty applications. Ethical trust-building, clinical implementation, and equitable innovation follow. Real-world examples from oncology, cardiology, neurology, and oral health demonstrate how blending radiology with pathology enhances cancer care, and how genetics combined with lifestyle and history guides precision therapies. With equal focus on ethics, fairness, and patient trust, the book emphasizes that progress must remain human-centered and responsible. It also highlights scalable innovation that can empower hospitals in major cities as well as resource-limited rural regions. Through clear, jargon-free explanations, readers learn how these advanced systems work behind the scenes — analyzing patterns across modalities to assist in diagnosis, prognosis, and treatment planning.

In the end, this book envisions a future where AI does not replace clinicians — it amplifies their empathy, expertise, and ability to heal.

What you will learn:

• Understand what multimodal AI is, its foundations, and why it matters
• Learn how multimodal AI works through data fusion across images, text, genomics, and clinical records
• Discover how integrated AI is transforming diagnosis, prognosis, and personalized treatment in medicine
• Explore the importance of ethics, trust, transparency, and fairness in AI-driven healthcare

Who this book is for:

Written for clinicians, researchers, students and healthcare leaders, this book offers a roadmap to the future of integrated diagnosis and personalized treatment.