Artificial Intelligence in Health Care: Revolutionizing Obstetrics and Gynecology

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Introduction

• AI Definition: Refers to machines designed to mimic human intelligence.
• Tasks AI can perform: From basic ones like reading to more complex ones like self-driving cars and generating human-like text (e.g., ChatGPT).
• Focus in Healthcare: AI could greatly improve patient care but needs careful implementation and oversight.

AI in Health Care: Key Areas

• Not a New Concept: Early medical AI models like MYCIN (1970s) were used for diagnosing infections.
• Recent Advancements: AI is now used in areas like:
  • Medical Imaging: Helps interpret X-rays, MRI scans, etc.
  • Telemedicine: Allows remote patient consultations.
  • Genomics: Aids in understanding genetic disorders.
  • Surgery: Robotic assistance in complex procedures.

Potential areas of AI in medicine
Clinician-facing	Patient-facing	Non-clinical
Diagnostic programs e.g. CTG interpretation	Symptom tracking e.g. in chronic disease control	Administrative tasks e.g. appointment scheduling
Treatment optimisation e.g. antibiotic selection	Pain management e.g. in neuropathic pain	Medical education e.g. virtual reality training
Image interpretation e.g. X-ray screening	Medical chatbots e.g. patient triage apps	Systematic review synthesis e.g. abstract screening
Robotic-assisted surgery	Telemedicine	Drug discovery

Non-Clinical Roles of AI in Medicine

• Medical Education: Virtual training programs for students.
• Research: Speeds up drug discovery and testing.
• Patient Access to Health Data: Tools like health apps and wearables let patients track their own health, promoting personalized care.

How AI Works in Healthcare

• Pattern Recognition: AI identifies patterns in large datasets to support decisions.
• Supervised Learning: AI learns from data labeled by humans (e.g., a dataset of images labeled as "cancerous" or "healthy").
• Real-World Example: AI can analyze cardiotocography (CTG) readings in obstetrics to detect fetal distress.

AI in Obstetrics and Gynecology

Examples of use-cases for AI in obstetrics and gynaecology
Obstetrics	Gynaecology
CTG interpretation	Endometriosis diagnosis
Ultrasound fetal age estimation & genetic screening	Computed tomography (CT) ovarian tumour detection
Ultrasound diagnosis of placenta accreta spectrum	Cervical cancer screening
Prediction of postpartum haemorrhage	Breast cancer therapy response analysis
Risk assessment for pre-eclampsia	Uroflowmetry interpretation

• Obstetrics:
  • CTG Interpretation: AI can analyze fetal heart rate patterns.
  • Ultrasound Analysis: Helps predict fetal age and genetic conditions.
  • Risk Assessment: For conditions like pre-eclampsia and postpartum hemorrhage.
• Gynecology:
  • Endometriosis Diagnosis: AI assists in interpreting laparoscopic images.
  • Cancer Screening: Detects cervical or ovarian cancers.

Case Study 1: AI for CTG Interpretation

• Problem: Human interpretation of CTG varies, leading to errors.
• Solution: AI models analyze heart rate patterns and classify them as normal, suspicious, or pathological.
• Impact: AI can support continuous fetal monitoring in real-time, enhancing early detection of complications.

Case Study 2: AI for Endometriosis Diagnosis

• Challenge: Laparoscopy (camera-based surgery) accuracy varies with human skill.
• Solution: AI uses image recognition to spot endometriosis in real-time.
• Results: A deep-learning AI achieved 99% accuracy, potentially reducing the need for tissue biopsies.

Benefits of AI in Healthcare

• For Patients:
  • Early disease detection.
  • Personalized treatment plans.
  • Better access to own health data.
• For Clinicians:
  • Reduced workload.
  • More time for patient care.
  • Decision support tools.
• For Healthcare Systems:
  • Increased capacity.
  • Lower costs.
  • Enhanced research capabilities.

Challenges of AI in Medicine

• Dataset Bias: AI may not perform well across diverse patient populations.
• Explainability: Many AI models (like deep learning) function as "black boxes," making it hard to understand their decisions.
• Legal Issues: Unclear who is responsible if AI makes a wrong decision.
• Patient Trust: Ensuring AI is safe and reliable to avoid loss of trust in healthcare providers.

Overcoming Challenges

• Solutions:
  • Improve diversity in datasets.
  • Push for more transparent AI models.
  • Strengthen data privacy regulations.
  • Train clinicians to work alongside AI effectively.
  • Build high-quality evidence to support AI use in practice.

Role of Clinicians in AI Integration

• Clinicians as Advocates: Ensure AI is used safely and ethically.
• Importance of Training: Doctors need to understand AI principles to spot errors and make informed decisions.
• Involving Clinicians in AI Development: Their real-world experience can guide AI applications in healthcare.

Conclusion

• Potential Impact: AI is set to transform healthcare by improving diagnostics, patient management, and operational efficiency.
• Need for Collaboration: Effective AI implementation requires collaboration between clinicians, developers, and policymakers.
• Focus on Training: Ensuring that healthcare professionals are educated and trained in AI will be key to realizing its benefits for patient care.

Potential benefits of AI in medicine
To patients	To clinicians	To systems
Earlier disease detection	Workload reduction	Increased capacity
Personalised treatment plans	Increased time for training	Reduced costs
Greater insight into own health data	Decision support	Enhanced medical research

Reference: https://doi.org/10.1111/tog.12950