AI in Modern Era: A Catalyst for Holistic Care or Not

Introduction

• Speaker: Clint Toral
• Hosted by students under the Doctor of Philosophy and Nursing program at the Philippine Women's University
• Topic: Exploring the role of AI in modern healthcare practices, particularly in nursing.

Doxology and National Anthem

• Welcoming remarks and a call for engagement from the audience.

Program Overview by Miss Joy Malai

• Key Focus Areas:
  • Integration of AI technologies in nursing practices.
  • Understanding the implications of AI on holistic patient-centered care.
• Critical assessment of AI's role as both a potential enhancer and challenge in nursing.

Opening Remarks by Miss Wilma Radera

• AI as an influential element in modern healthcare.
• Discussion on how AI can enhance clinical decision-making, streamline workflows, and personalize patient care.

Introduction of Speaker: Dr. Mark John Aral

• Education & Credentials:
  • BSN, MSN, two post-master’s degrees, Doctor of Nursing Practice.
  • Specialized in hematology and oncology nursing.
• Presentation Focus:
  • Current applications of AI in clinical practice.
  • Benefits and limitations of AI in enhancing holistic care.
  • Ethical considerations regarding AI in nursing.

AI Timeline Overview

• 1950s:
  • Birth of AI; Turing Test developed by Alan Turing.
• 1961:
  • Unimate, first industrial robot, launched.
• 1997:
  • IBM's Deep Blue defeats chess champion.
• 2011:
  • Introduction of Siri and IBM Watson.
• 2022:
  • Launch of ChatGPT; rapid user adoption.

AI Lifecycle

1. Data Creation
   • Importance of data in training AI algorithms.
2. Data Acquisition
   • Process of collecting relevant data.
3. Model Development
   • Clear definition of the problem AI aims to solve.
4. Model Evaluation
   • Assessing the performance and societal impact of AI models.

Relevance of AI in Healthcare

• **Techniques:
  • Natural Language Processing
  • Machine Learning
  • Robotics and Automation**
• Emphasis on ethical use and ensuring patient safety.

Application of AI in Clinical Practice

• Key Areas:
  • Screening and preventative care.
  • Risk assessment and management.
  • Nursing assessment and documentation.
  • Telemedicine and remote monitoring.
  • Differential diagnosis support.

Benefits of AI

• Key Advantages:
  • Real-time data management.
  • Improved treatment plans.
  • Enhanced accessibility for healthcare workers and patients.
  • Reduced medication errors.
  • Cost reduction in healthcare practices.

Limitations of AI

• Challenges:
  • Lack of effectiveness in real-world scenarios.
  • Data privacy risks.
  • Potential for bias leading to misdiagnosis.
  • High costs and dependency issues.

Ethical Considerations

• Key Focus Areas:
  • Healthcare justice and equity.
  • Ongoing research on AI integration in healthcare.
  • Protecting patient information and preventing misuse.

Conclusion

• Integration of AI must not compromise the core values of nursing.
• Importance of maintaining the human element in patient care alongside AI technologies.
• Continuous learning and adaptation by nursing professionals in response to AI advancements.

Q&A Session

• Questions regarding the effective role of AI in patient-centered care and potential future developments.
• Emphasis on AI's role in improving communication, especially with language barriers.

Closing Remarks by Miss Shuali

• Gratitude towards Dr. Aral for the insightful presentation.
• Encouragement for participants to apply lessons learned in their practice.

Acknowledgments

• Thanks to participants, faculty, and everyone who contributed to the webinar's success.

Final Notes

• Reminder for participants to fill out the evaluation form for certificates.
• Quick group photo opportunity to conclude the session.