What AI Can Help In - An AI cardiologist shares his perspective

Today I talked with a (AI) cardiologist and at the end of interview found how AI can be useful and where it would need more effort to get things done for the humans. The million dollar question is that, we are inputting so much personal information into these systems - whatever be the LLMs or created models. We are used to exploiting our own privacy to the level of the AI knowing all the diseases and ins and outs of who we are and we are not! Over to the interview now

Question: How AI Can Be Useful in Cardiology?

Early Diagnosis and Risk Prediction:

Electrocardiograms (ECG): AI algorithms can analyze ECG data more quickly and accurately than humans, identifying patterns that may indicate early signs of heart disease.

Imaging: AI can enhance the analysis of medical imaging like echocardiograms, MRIs, and CT scans, providing more precise measurements and identifying anomalies that may be missed by the human eye.

Personalized Treatment Plans:

AI can process vast amounts of patient data to create personalized treatment plans. By analyzing data from various sources, such as genetic information, lifestyle factors, and previous medical history, AI can suggest the most effective treatment protocols for individual patients.

Predictive Analytics:

AI can predict potential complications and readmission risks. By analyzing trends in patient data, AI can alert healthcare providers to patients who are at higher risk of adverse events, enabling proactive intervention.

Remote Monitoring and Telemedicine:

AI-powered devices and apps can monitor patients' heart health in real-time, sending data to cardiologists for continuous evaluation. This is particularly useful for patients with chronic heart conditions who need regular monitoring.

Assisting in Surgical Procedures:

AI can aid in planning and executing complex cardiovascular surgeries. For instance, AI can help create detailed 3D models of a patient’s heart, assisting surgeons in planning the procedure more accurately.

Where AI Can Do a Better Job:

Data Analysis and Pattern Recognition:

AI can process and analyze large datasets more efficiently than humans, identifying subtle patterns and correlations that may not be evident to even the most experienced cardiologists.

Speed and Efficiency:

AI can quickly analyze test results and imaging data, providing faster diagnoses and allowing cardiologists to spend more time on patient care rather than data interpretation.

Consistency and Accuracy:

AI algorithms are not subject to fatigue or cognitive biases, which can affect human judgment. This consistency can lead to more accurate diagnoses and treatment recommendations.

Practical Uses Based on Equipment and Technology:

Wearable Devices:

Devices like smartwatches and fitness trackers equipped with AI can monitor heart rate, detect irregularities, and alert both the patient and healthcare provider if there are signs of potential issues such as atrial fibrillation.

AI-Enhanced Imaging Tools:

AI software integrated with imaging equipment can provide more precise measurements and highlight areas of concern in real-time, assisting cardiologists during examinations and procedures.

Telehealth Platforms:

AI-driven telehealth platforms can facilitate virtual consultations, where AI can pre-screen patient data and provide preliminary analysis, making the consultation more efficient and focused.

Decision Support Systems:

AI-based decision support systems can provide cardiologists with evidence-based recommendations and treatment options, helping them make more informed decisions.

By leveraging AI, cardiologists can enhance the accuracy of diagnoses, improve patient outcomes, and increase the overall efficiency of cardiac care.

Question: Time and effort to have perfect AI for Diagnosis

What would be the amount of training that we might need to train it on ECGs and images to make it better at diagnosis Doctor? I think we would still need a doctor or a medical expert to feed the AI with details before it can give us a fair % of accuracy in diagnosis. What % of accuracy from would suit to be full replacing a human dianosing a disease?

Training an AI model to accurately diagnose ECGs and medical images requires a substantial amount of high-quality data and collaboration with medical experts. Here's a breakdown of the training process, the involvement of medical professionals, and the accuracy levels required for deployment:

Amount of Training Required:

1. Data Collection:

- ECG Data: Thousands to millions of ECG recordings with corresponding diagnoses and outcomes are necessary. Diverse data, including various conditions, ages, genders, and ethnic backgrounds, help create a robust model.

- Medical Images: Similarly, tens of thousands to millions of annotated images (echocardiograms, MRIs, CT scans) are required to cover a wide range of cardiac conditions.

2. Annotation and Labeling:

- Expert Involvement: Cardiologists and medical experts need to annotate the data, ensuring that each ECG and image is accurately labeled with the correct diagnosis. This step is crucial for supervised learning.

- Quality Control: Regular reviews and audits of the annotated data by multiple experts help maintain high standards and reduce errors.

3. Model Training:

- Algorithms: Various machine learning and deep learning algorithms, such as convolutional neural networks (CNNs) for images and recurrent neural networks (RNNs) for time-series data like ECGs, are employed.

- Computational Resources: High-performance computing resources, including GPUs and TPUs, are used to train the models on large datasets.

4. Validation and Testing:

- Split Datasets: The data is split into training, validation, and test sets. Cross-validation techniques help ensure the model's generalizability.

- Performance Metrics: Metrics like accuracy, sensitivity, specificity, precision, and recall are used to evaluate the model.

Expert Involvement:

- Initial Training: Medical experts are essential during the initial training phase to provide accurate annotations and guide the model development.

- Continuous Improvement: Even after deployment, ongoing feedback from cardiologists helps fine-tune the model, addressing any inaccuracies and improving performance.

Accuracy Levels for Deployment:

- Clinical Decision Support: An AI model used as a decision support tool, assisting doctors rather than replacing them, typically requires high sensitivity and specificity (over 90%) to be clinically useful without causing harm.

- Full Replacement: For an AI system to fully replace a human in diagnosing diseases, it would need to achieve near-human performance levels, typically above 95% accuracy, along with high sensitivity and specificity. It must also demonstrate reliability and robustness across diverse patient populations and conditions.

Regulatory and Ethical Considerations:

- Regulatory Approval: AI models for medical diagnosis must undergo rigorous testing and receive approval from regulatory bodies like the FDA or EMA. This process ensures the model's safety, efficacy, and reliability.

- Ethical Concerns: Transparent algorithms, explainability, and accountability are essential to address ethical concerns. The AI should provide interpretable results that doctors and patients can understand.

Practical Implementation:

1. Pilot Programs: Before full deployment, AI models are often tested in pilot programs within hospitals and clinics to evaluate real-world performance.

2. Continuous Monitoring: Post-deployment, continuous monitoring and periodic re-evaluation of the model's performance ensure it remains accurate and reliable.

In summary, training an AI for ECG and medical image diagnosis requires extensive data, expert involvement, and rigorous testing. While AI can significantly enhance diagnostic accuracy and efficiency, achieving a level of performance suitable for fully replacing human doctors requires near-perfect accuracy and robust validation through clinical trials and regulatory approval.