BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models

arXiv:2510.15866 · cs.CV, cs.NE · Submitted 2025-10-17 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models".

Tom: The gist The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts.

Jane: First, who's behind it and why it matters.

Title and authors: Tom: So, to recap what BiomedXPro actually does, it’s this evolutionary framework built around a large language model designed specifically for biomedical diagnosis.

Jane: It takes the problem of uninterpretable prompts or just single prompts and replaces it with an evolving ensemble of natural language prompts.

Lu: The summary emphasizes that these prompts aren't random; they are carefully crafted to emphasize specific morphological alterations, tissue organizational patterns, or cellular-level abnormalities that are directly interpretable by clinical practitioners.

Meng: Essentially, the framework uses the LLM as a dual engine: it extracts the biomedical knowledge needed and then adapts those prompts iteratively to boost diagnostic performance.

Tom: It’s not just about finding one good prompt; it’s about finding many complementary prompts that each capture a different, important aspect of what’s happening in the image.

Jane: This diversity is key because it mimics how human clinicians look at evidence—they don't just see one thing, they integrate multiple observations.

Lu: The framework is structured to provide three main benefits: interpretability, diversity, and clinical trustworthiness when it comes to using these vision language models for medical diagnosis.

Tom: Those three points are what make it so different from older methods that just tried to tune a single prompt vector or a few fixed prompts.

Jane: They are showing that by evolving the prompts this way, you anchor your AI predictions in concepts that have real clinical meaning, which builds trust.

Meng: The authors stress that their approach goes beyond just chasing accuracy and instead targets making the model’s reasoning understandable within a medical context.

Lu: They are leveraging LLMs to generate these semantically meaningful prompts that can be automatically refined through structured feedback mechanisms during the optimization process.

Tom: So, they are essentially using the LLM's vast biomedical knowledge to guide the entire prompting evolution towards a more clinically relevant outcome.

Jane: And they’re showing that this method consistently outperforms state-of-the-art prompt tuning methods, especially when you have very little training data available.

Meng: That performance in data-scarce environments is where the practical impact really starts to show up for developers who are trying to deploy these tools quickly.

Lu: The paper sets up a framework where the AI doesn't just give an answer but shows its work by providing human-readable justifications for that answer, which is a big step forward.

Tom: So, the main idea is moving from black-box prompts to an evolving ensemble of interpretable, diverse prompts for better diagnostic results.

The paper's summary: Jane: Now that we’ve talked about what it does, let’s look at the specific improvements they are proposing for this BiomedXPro framework.

Tom: They highlight four key areas of improvement, and they focus heavily on how to enhance interpretability by making sure those prompts aren't just abstract vectors.

Lu: The first improvement is about moving from a single optimal prompt to generating a diverse ensemble of human-readable prompts that capture distinct diagnostic observations.

Jane: This means instead of one abstract vector, you get multiple text descriptions, each focusing on different visual cues like tissue patterns or specific alterations.

Meng: That directly addresses the limitation where existing methods often produce singular prompts that can't capture the complexity of a medical diagnosis.

Tom: The second improvement is grounding the predictions in clinical trust by ensuring these prompts are semantically meaningful medical concepts rather than just mathematical artifacts.

Jane: This means they’re showing a strong semantic alignment between those discovered prompts and statistically significant clinical features, which makes the AI's decision-making verifiable.

Lu: The third improvement is maintaining diversity, which they achieve through a crowding mechanism inspired by NSGA-II to eliminate any redundant prompts in the final candidate pool.

Tom: So, they aren't just generating diverse prompts and then hoping the best; they’re actively managing that diversity to keep it clean and meaningful.

Jane: And the fourth improvement is automated feature articulation from statistical significance, where the framework can automatically discover and articulate features by correlating them with conditional probabilities.

Meng: That allows the model’s performance to be grounded in verifiable concepts, which helps bridge that gap between statistical performance and actual medical understanding.

Tom: So, they are saying this isn't just a new way to tune prompts; it’s a structured system for discovering and articulating medically relevant features automatically.

The paper's improvements: Jane: So we’ve covered the BiomedXPro framework today, and the final summary is that this work represents a significant step toward making vision language models safe for clinical deployment.

Tom: It moves us past the problem of uninterpretable latent vectors and single text prompts by introducing an evolutionary approach.

Lu: The main implication is that we’re getting systems where the AI can show its work through human-readable justifications instead of just giving a final diagnosis without explanation.

Meng: For practical application, this means these models can be integrated into established diagnostic workflows because their outputs are anchored in concepts that doctors already recognize and use.

Tom: We’ve seen strong results in the data-scarce few-shot regime, which is crucial for real-world scenarios where labeled data is hard to come by.

Jane: The final word on BiomedXPro is that it provides a structured path toward reliable deployment of advanced vision language models in clinical practice.

Lu: It’s a major step in ensuring that the AI tools we use are not just accurate but also transparent and trustworthy for high-stakes diagnostic settings.

Meng: We’ll keep an eye on how this framework evolves, because getting those visual grounding analyses done will be key to fully verifying the model’s sensitivity to subtle details.

Tom: And that wraps up our discussion on BiomedXPro for now.

Conclusion: Tom: So, we’re wrapping up on BiomedXPro, which is essentially this framework that uses a large language model to evolve multiple natural language prompts instead of just finding one single prompt for medical diagnosis.

Jane: Right, so it’s about moving away from those uninterpretable vectors and singular prompts toward an ensemble of diverse, medically grounded descriptions that each capture different diagnostic details.

Lu: It really clever how they use the LLM not just to extract knowledge but also to act as the optimizer that iteratively refines these prompts through a structured feedback loop.

Meng: From an engineering standpoint, it’s interesting how they set up this multi-objective optimization problem balancing classification accuracy with prompt diversity within the VLM embedding space.

Lalam: I can see how this capability means our vision models could start providing justifications for their predictions that actually make sense to a clinician.

Tom: And the results are pretty compelling, showing consistent outperformance in those few-shot settings where data is scarce, which is huge for real clinical use right now.

Jane: They also show a strong link between those discovered prompts and actual clinical features, like capturing specific vascular structures or nucleus shapes with high statistical significance.

Lu: That alignment analysis is what really seals the deal for me; it proves that the evolutionary process isn't just finding good-looking text, but actually articulating statistically significant visual features.

Meng: The limitation they point out is that this whole thing relies heavily on how well the underlying LLM encodes biomedical knowledge, so if the knowledge base is weak, those prompts won’t be very useful.

Lalam: That means the quality of our medical understanding is directly tied to what we feed into these systems first.

Tom: So, BiomedXPro shows a clear path toward deploying vision language models in a way that’s not just accurate but also transparent and trustworthy for diagnosis.

Jane: It really demonstrates how combining LLMs with VLM adaptation can solve the problem of making AI reasoning understandable for medical professionals.

Lu: It’s definitely a big step toward building systems where you can see *why* the model made a certain call in an image.

Meng: Next up, we’re looking at how other models handle uncertainty, so we'll be diving into that next.

University of Peradeniya

cs.CV, cs.NE

Submitted: 2025-10-17

Updated: 2026-10-07

Importance score: 83/100

The gist: The gist The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts.

Key concepts

Prompt Optimization Techniques: These are methods used to refine text inputs given to vision-language models (VLMs) to improve their performance. The paper notes that current techniques often result in uninterpretable mathematical vectors or just one simple text prompt, which limits clinical trust.
BiomedXPro Framework: This is a system designed for biomedical diagnosis that uses an LLM to act as both a knowledge extractor and an optimizer. It iteratively evolves several different, human-readable prompts instead of settling on just one, aiming for better performance and transparency.
Multi-objective Optimization: This is the mathematical process used within BiomedXPro to find the best prompts. The goal is to balance two competing things: achieving high accuracy in diagnosis and ensuring that the generated prompts are diverse enough to capture many different, important medical features.

Terminology

Accuracy — 11× in this paper · explained in 1 episode
Generalization — 11× in this paper · explained in 13 episodes
Interpretability — 11× in this paper · explained in 15 episodes
Alignment — 10× in this paper · explained in 1 episode
Benchmark — 9× in this paper · explained in 2 episodes
Grounding — 8× in this paper · explained in 3 episodes
Adaptation — 7× in this paper · explained in 1 episode
Semantic Alignment — 5× in this paper · explained in 3 episodes
Binary Cross-Entropy — 3× in this paper
Chain-of-Thought (CoT) — 3× in this paper · explained in 4 episodes
Chain-of-Thought Prompting — 3× in this paper · explained in 2 episodes
Core — 3× in this paper · explained in 2 episodes
Effect of Chain-of-Thought — 3× in this paper
Fine-Tuning — 3× in this paper · explained in 5 episodes
Multi-objective Optimization — 3× in this paper · explained in 4 episodes
Redundancy — 3× in this paper · explained in 2 episodes
Visual Grounding — 3× in this paper · explained in 2 episodes
Abundance — 2× in this paper · explained in 1 episode
Adaptability — 2× in this paper · explained in 1 episode
Chain-of-Thought (CoT) Prompting — 2× in this paper · explained in 3 episodes
Clustering — 2× in this paper · explained in 2 episodes
Contrastive Language-Image Pre-training — 2× in this paper
Cosine Similarity — 2× in this paper · explained in 3 episodes
Explainability — 2× in this paper · explained in 3 episodes
Few-Shot Learning — 2× in this paper · explained in 4 episodes
Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
Prompt Engineering — 2× in this paper · explained in 4 episodes
Robustness — 2× in this paper · explained in 5 episodes
Semantic Similarity — 2× in this paper · explained in 2 episodes
Verification — 2× in this paper · explained in 2 episodes
Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
Ablation — 1× in this paper · explained in 1 episode
Ablation Studies — 1× in this paper · explained in 1 episode
Computer Vision and Pattern Recognition — 1× in this paper
Contrastive Learning — 1× in this paper · explained in 5 episodes
Distribution Shift — 1× in this paper · explained in 5 episodes
Embeddings — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Transferability — 1× in this paper · explained in 4 episodes

Summary

The gist The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts.

BiomedXPro Framework

BiomedXPro is an evolutionary framework that leverages a large language model as both a biomedical knowledge extractor and an adaptive optimizer to automatically generate a diverse ensemble of interpretable, natural-language prompt pairs for disease diagnosis. This approach addresses the limitations of conventional methods by evolving multiple prompts, each capturing distinct diagnostic observations such as specific morphological alterations or tissue organizational patterns BiomedXPro consistently outperforms state-of-the-art prompt-tuning methods, particularly in data-scarce few-shot settings. The framework is designed to deliver three key advantages for clinical integration: Interpretability, Diversity, and Clinical Trustworthiness.

Methodology and Optimization

The methodology formulates prompt discovery as a multi-objective optimization problem that balances classification accuracy with prompt diversity. This is achieved through an evolutionary algorithm that operates directly within the VLM embedding space. The process involves several key steps:

Initialization, where a meta-prompt Q0 encodes key diagnostic observations to initialize the first-generation population.
Fitness evaluation, where each prompt pair is evaluated on the training set using a performance metric M.
Prompt pairs with sj ≥ α are retained in a memory buffer U(t).
LLM-guided mutation, where an LLM is instructed via meta-prompt Qt to create Kt new prompt pairs that capture distinct medical concepts and yield better performance.
Crowding for Diversity, where a crowding mechanism inspired by NSGA-II is applied to the final candidate pool U(T) to eliminate semantic redundancy.

Experimental Results and Clinical Relevance

Experiments on multiple biomedical benchmarks show that BiomedXPro consistently outperforms all baselines in the data-scarce few-shot regime (Table 1). Its advantage is most pronounced in the critical 1–8 shot range, demonstrating strong generalization across diverse tasks and imaging modalities. Furthermore, analysis demonstrates a strong semantic alignment between the discovered prompts and statistically significant clinical features. For Derm7pt, the analysis reveals a strong correlation where ‘Linear Irregular’ vascular structures (P = 0.80) was captured by a high-fitness prompt pair (F1: 0.6523) contrasting a ’regular, linear arrangement’ with a ’chaotic, branching pattern’. Similarly, for WBCAtt classification, the framework captured the most predictive features such as the presence of small granules (P = 1.00) and the unsegmented-band nucleus shape (P = 0.85). This consistent alignment across tasks demonstrates that the framework’s evolutionary process discovers and textually articulates statistically significant visual features in an interpretable manner.

Limitations and Future Directions

The efficacy of BiomedXPro is fundamentally dependent on two core components, the underlying LLM and VLM. The framework’s ability to generate clinically relevant prompts is inherently capped by the breadth and accuracy of the biomedical knowledge encoded within the LLM, creating a potential knowledge bottleneck. Architecturally, there are limitations; for instance, diversity is enforced via crowding only as a final postprocessing step because per-iteration LLM-driven clustering was unstable. Future work should focus on building full clinical trust by verifying that the VLM’s decision-making is truly sensitive to nuanced details through visual grounding analysis using methods like Grad-CAM.

The paper concludes that BiomedXPro represents a significant step toward the safe and reliable deployment of advanced visionlanguage models in clinical practice.

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BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models Kaushitha Silva, Mansitha Eashwara, Sanduni Ubayasiri, Ruwan Tennakoon, Damayanthi Herath Abstract The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on integrating diverse observations, limits their trustworthiness in high-stakes settings

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Introduction Accurate and transparent interpretation of biomedical images is fundamental for reliable disease diagnosis In clinical practice, radiologists, pathologists, and other medical specialists rely on well-established visual cues such as cellular morphology, tissue architecture, and pathological patterns, combined with domain expertise to make informed diagnostic decisions For computer vision systems to achieve clinical acceptance, they must not only demonstrate high predictive accuracy but also provide interpretable outputs that align with established clinical reasoning processes Recent advances in Vision-Language Models (VLMs), particularly Contrastive Language-Image Pre-training (CLIP) [14], have demonstrated remarkable potential for bridging visual content with natural language descriptions Biomedical adaptations such as BiomedCLIP [21] extend these capabilities to medical imaging domains While CLIP models demonstrate strong zero-shot capabilities, their performance often benefits from prompt optimization tailored to specific tasks Early attempts relied on manual prompt engineering [14], which was labor-intensive and required domain expertise To overcome these challenges, gradient-based prompt learning methods such as Context Optimization (CoOp) [24] introduced learnable soft prompts represented as continuous vectors optimized via gradient descent Biomedical adaptations, including BiomedCoOp [10] and XCoOp [1] incorporated domain knowledge into CoOp frameworks to improve performance Despite these advances, two critical challenges remain for clinical adoption: (1) prompts used to guide these models are typically optimized as uninterpretable feature vectors, providing no insight into the underlying diagnostic rationale [1, 3, 10], and (2) existing methods generally produce singular prompts, restricting their ability to capture the multifaceted nature of clinical observations that practitioners routinely consider Large Language Models (LLMs) present a compelling solution to these limitations They can generate semantically meaningful, natural-language prompts that encode biomedical knowledge and can be automatically refined to enhance diagnostic performance [20, 25] However, existing LLM-driven prompt optimization techniques still operate largely as black-box systems, with limited mechanisms to ensure clinical transparency or domain relevance In this work, we introduce BiomedXPro, an evolutionary prompting framework specifically designed for biomedical disease diagnosis Unlike conventional methods that seek a single optimal prompt, our framework evolves a diverse ensemble of human-readable prompts, each capturing distinct diagnostic observations These prompts may emphasize specific morphological alterations, tissue organizational patterns, or cellular-level abnormalities that are directly interpretable by clinical practitioners We leverage LLMs both as biomedical knowledge extractors and as adaptive optimizers that iteratively refine prompts through structured feedback mechanisms, ensuring that the final prompt ensemble captures a comprehensive range of clinically meaningful features Our approach delivers three key advantages for clinical integration: Interpretability, Diversity, and Clinical Trustworthiness 1. Interpretability: Each optimized prompt corresponds to a clear, medically grounded observation, providing transparency into model decision-making processes and enabling clinical validation 2. Diversity: Maintaining multiple complementary prompts mirrors the multi-perspective approach that clinicians naturally employ when evaluating diagnostic evidence, enhancing model robustness and generalization capabilities 3. Clinical Trustworthiness: Probabilistic predictions are anchored in semantically meaningful medical concepts, facilitating their integration into established diagnostic workflows and supporting evidence-based clinical decision-making By combining the adaptability of vision-language models with interpretable, LLM-driven prompt evolution, our framework goes beyond conventional accuracy-focused approaches and directly addresses the fundamental barriers to safe and trustworthy deployment of AI systems in clinical diagnostic environments

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Related work 2.1. VLMs in biomedical imaging VLMs like CLIP [14] have revolutionized multi-modal learning by aligning images and text through contrastive pre-training, enabling strong zero-shot capabilities However, their direct application to the biomedical domain is challenged by specialized terminology, subtle visual markers, and the scarcity of labeled data [22] To overcome these limitations, domain-adapted models such as MedCLIP [18], PubMedCLIP [4], and BiomedCLIP [21] have been developed BiomedCLIP, trained on over 15 million biomedical image-text pairs with a PubMedBERT encoder, has notably established state-of-the-art results across multiple biomedical vision-language benchmarks While it offers strong zero-shot performance, its effectiveness can be further enhanced by adapting it to specific tasks This is where prompt tuning becomes critical, as it efficiently tailors a frozen VLM for a new task without full model fine-tuning This method involves optimizing a text prompt or its token level representation to guide the model toward the most relevant information, thereby capturing the fine-grained, diseasespecific nuances that are essential in clinical applications and enhancing performance even in low-data settings 2.2.

Improvements for AI systems

Bold header: Evolutionary Prompt Optimization for Diverse Interpretability

This system can generate a diverse ensemble of interpretable, natural-language prompt pairs instead of a single textual prompts, which addresses the limitation that existing methods generally produce singular prompts, restricting their ability to capture the multifaceted nature of clinical observations.

Bold header: Grounded Prediction and Clinical Trustworthiness

The improved system's predictions are anchored in semantically meaningful medical concepts, facilitating their integration into established diagnostic workflows because the analysis demonstrates a strong semantic alignment between the discovered prompts and statistically significant clinical features.

Bold header: Robust Few-Shot Learning Performance

BiomedXPro is shown to consistently outperform state-of-the-art prompt-tuning methods, particularly in data-scarce few-shot settings, enabling reliable performance when labeled data is limited.

Bold header: Automated Feature Articulation from Statistical Significance

The framework can automatically discover and articulate statistically significant visual features by correlating them with conditional probabilities like P(class observation), grounding the model’s performance in verifiable concepts.