AI in Healthcare: Innovations, Insights, and Real-World Applications

Note: This article was generated by Microsoft Copilot using a transcript from our recent webinar, “AI in Healthcare: Innovations, Insights, and Real-World Applications.” The author reviewed and edited the output.

Artificial Intelligence (AI) is revolutionizing various industries, and health care is no exception. A recent webinar hosted by the MUSC Master of Science in Health Informatics program, presented by faculty member, Dr. Larry Norman and moderated by program director, Dr. Abby Kazley, highlighted the profound impact of AI on health care, covering topics from cybersecurity to predictive analytics and medical imaging.

Generative AI and Its Applications

Generative AI, as defined by IBM, is a deep learning model that generates high-quality text, images, and content based on the data it has been trained on. During the webinar, Norman emphasized the importance of prompting, which involves using scripted directions to achieve desired outputs from AI models. Popular generative AI tools include ChatGPT, Microsoft Copilot, Google Gemini, and Amazon's CodeWhisperer. These tools are freely available and have become integral in various applications, including health care.

AI in Cybersecurity

AI has a dual role in cybersecurity. It can be used by hackers to automate attacks, making spam, phishing, malware, and ransomware activities more efficient. Conversely, security professionals leverage AI to identify and respond to these threats quickly. AI also aids in creating adaptive malware and collecting social engineering data, which complicates detection and prevention efforts. Given the sensitive nature of health data, robust cybersecurity is essential to prevent breaches that could result in substantial fines and misuse of personal information.

Predictive Analytics in Health Care

Predictive analytics is another area where AI is making significant strides. During the COVID-19 pandemic, an AI model was developed using wastewater treatment surveillance to predict outbreaks. This model could forecast COVID-19 surges up to two weeks in advance, allowing hospitals to better prepare their staffing and supplies. Such predictive models can be extended to other communicable diseases and flu outbreaks, providing valuable insights for health care providers.

Genomics and Personalized Medicine

AI is also transforming genomics, helping researchers understand genetic traits and susceptibilities to diseases. Programs like MUSC's In Our DNA aim to gather DNA data to predict health trends and develop targeted treatments. AI can assist in identifying genetic markers in unborn babies and tailor treatments for conditions like cancer, enhancing personalized medicine.

Medical Imaging and Diagnostics

Medical imaging is one of the fastest-growing areas for AI applications. AI can help detect conditions like diabetic retinopathy and breast cancer earlier than traditional methods. For instance, AI analysis of mammograms has significantly reduced false negative rates, potentially saving lives by identifying cancer cases that might have been missed. German startup Vera's collaboration with a mammography reference center in Oldenburg demonstrated the effectiveness of AI in improving diagnostic accuracy.

Challenges and Ethical Considerations

Despite its benefits, AI in health care faces challenges, including the risk of hallucinations—instances where AI provides incorrect or non-existent information. These errors can arise from incomplete data, biases in training data, and overgeneralization. Ensuring the accuracy and reliability of AI outputs is crucial, especially in medical settings where incorrect information can have serious consequences.

AI is revolutionizing health care by providing advanced tools for diagnostics, personalized treatments, and improved cybersecurity. However, it is crucial to approach AI with caution, ensuring the accuracy of its outputs and addressing ethical concerns. As AI technology advances, its integration into health care holds the promise of enhancing patient outcomes and optimizing medical processes, leading to a more efficient and effective health care system.

The MUSC Master of Science in Health Informatics (MSHI) program integrates AI and other relevant topics into its curriculum, preparing students to leverage cutting-edge technology in health care settings. Courses cover data science, information management, and more, providing a comprehensive understanding of how AI can be applied to improve patient care and health care operations. Learn more about the MSHI program.

Access the full webinar recording.