Predictive analytics in healthcare Volume 2 transforming the future of medicine Vinithasree Subbhuraam

Format:

Book

Author/Creator:

Subbhuraam, Vinithasree, author.

Contributor:

Institute of Physics (Great Britain), publisher.

Series:

IPEM-IOP series in physics and engineering in medicine and biology

Language:

English

Subjects (All):

Medical informatics.

Medicine--Data processing.

Medicine.

Predictive analytics.

Forecasting.

Medical Informatics.

Data Science.

Medical Subjects:

Medical Informatics.

Data Science.

Forecasting.

Physical Description:

1 online resource

Other Title:

Transforming the future of medicine.

Place of Publication:

IOP Publishing 2025

Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) IOP Publishing [2025]

Biography/History:

Dr. Vinithasree Subbhuraam is a digital health innovator and 0 to 1 product expert with over 15 years of experience planning and executing game-changing ideas and launching multiple industry-first AI/ML health tech products, especially wearable technologies. Dr. Subbhuraam is also an experienced researcher and mentor, particularly adept at designing and developing digital health solutions for highly complex technical and scientific problems that directly impact global healthcare. She has over 100 publications in high-impact factor peer-reviewed international journals, conferences, and books. Her work on breast cancer has been cited in World Health Organization Handbooks on Cancer Prevention

Summary:

The purpose of this book is to help the reader understand academic entrepreneurship and the steps required to bring more research from the lab to real life, as well as to explore the opportunities of applying generative AI in medical education, such as curriculum development, teaching methodologies, personalized learning, learning assessment, medical program tracking, and medical research. It also presents ten ways how AI can improve the adoption of integrative medicine by discussing some successful case studies. The book also discusses the latest advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer, from risk assessment and stratification to screening, diagnosis, treatment, and management. It also presents the four most innovative AI-powered solutions that have disrupted precision medicine. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology

Contents:

1. From lab to life : navigating academic entrepreneurship to revolutionize health tech

1.1. Introduction

1.2. Typical roadmap of academic entrepreneurship

1.3. The valley of death

1.4. Strategies to cross the valley of death

1.5. Tips from a successful academic entrepreneur

1.6. A real-world case study

1.7. Final remarks

2. Can generative AI change how we deliver medical education?

2.1. Introduction

2.2. Opportunities

2.3. Challenges and recommendations

2.4. Conclusions and future directions

3. Digital transformation in complementary and alternative medicine

3.1. Introduction

3.2. The intersection of AI and CAM

3.3. Case studies of applications of AI in CAM

3.4. Some notable companies in integrative medicine

3.5. Challenges

3.6. Conclusions and future directions

4. AI-powered solutions for prostate health management-risk assessment, screening, diagnosis, treatment, and management : a review

4.1. Introduction

4.2. Risk assessment and screening

4.3. Prostate cancer diagnosis and risk stratification

4.4. Prostate cancer treatment planning

4.5. LLM applications

4.6. FDA/CE-approved commercial AI applications

4.7. Conclusions

5. How is AI transforming precision medicine?

5.1. Introduction

5.2. Digital twins

5.3. Multi-omics integration

5.4. AI-driven biomarker discovery

5.5. Personalized therapeutics

5.6. Conclusions

Notes:

"Version: 20250501"--Title page verso

Includes bibliographical references

Online resource; title from PDF title page (viewed on June 2, 2025)

Other Format:

Print version Predictive analytics in healthcare

ISBN:

9780750323178

9780750323161

OCLC:

1521952566

Access Restriction:

Restricted for use by site license