How can you measure generative AI model effectiveness for personalized medicine?

1 Challenge 1: Data quality and diversity

Measuring the effectiveness of generative AI models for personalized medicine presents a major challenge due to the quality and diversity of the data produced. This data must be realistic, relevant, and representative of the target population and problem. For instance, if the model generates synthetic images of skin lesions, they should appear like real skin lesions and encompass a variety of types, stages, and locations. Likewise, if the model generates molecules for drug discovery, they should have valid chemical structures and desirable characteristics such as solubility, toxicity, and activity.

To measure the quality and diversity of the data, some common metrics are fidelity, diversity, and novelty. Fidelity assesses how closely generated data matches real data distribution; this can be done by comparing generated and real images with an inception score or Fréchet inception distance. Diversity gauges how diverse and dissimilar generated data samples are; this can be evaluated with a coverage or mode score. Novelty measures how original generated data samples are; this can be done by assessing uniqueness or KL divergence to determine how many generated data samples are not present in the real data set or how much they differ from it.

2 Challenge 2: Data utility and validity

Measuring generative AI model effectiveness for personalized medicine presents a challenge, as the data produced must be both useful and valid for the specific task and goal. For example, if the model generates synthetic text for medical diagnosis, it should provide accurate and relevant information and recommendations for the patient. Similarly, if the model generates synthetic gene sequences for gene therapy, they should have functional and beneficial effects on the target cells or organs. To measure the data utility and validity, two possible metrics are task performance and domain knowledge. Task performance evaluates how well the generated data can improve or enhance the performance of a downstream task or application. Domain knowledge measures how well the generated data conforms to domain-specific rules and constraints. For example, grammar, syntax, or semantics can be used to evaluate how well generated text follows medical terminology and logic. Molecular weight, logP, or Tanimoto similarity can be used to evaluate how well generated molecules comply with drug-likeness criteria.

3 Challenge 3: Data interpretability and explainability

A third challenge for measuring generative AI model effectiveness for personalized medicine is the interpretability and explainability of the data that the model can produce. To ensure that this data is understandable and transparent for users and stakeholders, such as patients, doctors, researchers, or regulators, it should show clear and consistent patterns and features which experts can interpret and explain. For instance, if the model generates synthetic images of brain scans, they should be easily visualized with saliency maps, heat maps, or contour plots to highlight important regions or factors. Similarly, if the model generates synthetic patient profiles, they should provide clear and consistent reasons and evidence for the personalized treatment or intervention. To measure data interpretability and explainability, potential metrics include visualization (using saliency maps or heat maps), evaluation (using human ratings or feedback), and evaluation (using clinical trials or tests).

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?