Advancing Healthcare with GenerativeAI: A Multifaceted Approach to Reliable Medical Information and Innovation

The rapid advancements in Artificial Intelligence (AI) have transformed the healthcare industry, reshaping the way we approach patient care, medical research, and healthcare delivery. This thesis explores the journey of AI in healthcare, from its early beginnings to the current landscape of highly sophisticated conversational AI systems. We first delve into the myriad applications of GenAI and AI in healthcare, including medical imaging analysis, drug discovery, personalized medicine, conversational chatbots and beyond. Through a series of case studies and real-world examples, the thesis illustrates the successes, challenges, and lessons learned from the implementation of AI in various healthcare settings. As we navigate the uncharted territory of AI in healthcare, we critically examine the ethical implications that arise and the regulations needed. Looking towards the future, we explore the bright promise and cautionary tales that lie ahead. While the continued advancements in technology hold the potential to revolutionize disease prevention, personalize treatments, and unlock new frontiers in medical research, we must remain vigilant about the risks and unintended consequences that may arise. Central to this thesis is the introduction of a novel technology and product we developed to address the reliability of large language models (LLMs) in healthcare: Veracity-Health. By enhancing the trustworthiness and accuracy of these models, this innovative approach aims to facilitate the responsible and confident deployment of AI for the benefit of patients and physicians. This thesis aims to provide a rigorous analysis of the applications, innovations, and ethical considerations surrounding AI in healthcare. By contributing to the ongoing discourse, we hope to shape a future where the power of artificial intelligence is harnessed for the greater good, prioritizing reliability and integrity of GenAI implementation in healthcare.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Bennani, Taieb

Advisor dc:contributor.advisor

• Fazel-Zarandi, Mohammad

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156048

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156048