One of our healthcare clients was looking to enhance the precision and personalisation of their patient care recommendations using artificial intelligence (AI). They wanted an AI model that could adapt to individual patient needs and deliver more accurate treatment suggestions. These case studies reflect the challenges we faced and solutions we implemented to overcome those.
Challenges
The existing AI model faced several challenges:
- Lack of Personalisation: The AI was trained on generalised data, which didn't account for the unique medical history of each patient.
- Limited Accuracy: It wasn't fine-tuned for specific health conditions or individual patient variables, resulting in sub-optimal treatment recommendations.
- Scalability Issues: As the client's patient database grew, the AI struggled to deliver accurate recommendations for all cases, especially rare and complex conditions.
Our Approach & Solution
We took a step-by-step approach to fine-tune the AI model:
- Data Customization: We used advanced data labelling techniques to train the AI with a broader set of patient-specific health data, including medical histories, diagnoses, and treatments.
- Transfer Learning: By applying transfer learning methods, we adapted the model to recognize patterns in smaller datasets related to specific diseases.
- Reinforcement Learning: We added reinforcement learning techniques to continuously improve the AI's decision-making abilities based on real-time patient feedback and outcomes.
- Cross-Validation: To ensure the model's accuracy, we implemented a cross-validation system where the AI was repeatedly tested on different subsets of patient data.
What Difference We Made
The fine-tuned AI provided:
- Personalized Care Plans: Tailored treatment recommendations based on individual patient profiles.
- Improved Decision-Making: Doctors were equipped with AI-driven insights, helping them make faster and more accurate medical decisions.
- Scalable Accuracy: The AI could now handle a diverse range of patient cases, from common ailments to rare diseases.
Impact of Delivery
- 35% increase in treatment recommendation accuracy
- 20% reduction in the time taken for diagnosis
- Higher patient satisfaction, as care plans became more personalized
Conclusion
Our fine-tuning efforts transformed the AI into a powerful tool for personalized patient care, allowing our client to offer better treatment plans and enhance patient outcomes, while also improving the scalability of the AI system as their patient base grew.
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