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ARXIV:2601.16549 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.16549MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks.
Opportunity summary
Pain A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities…
The combination of multimodal Vision-Language Models (VLMs) and Large Language Models (LLMs) opens up new possibilities for medical classification. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. According to our results, traditional machine learning (ML) models set a high standard by consistently achieving the best overall performance across most medical categorization…
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks.
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10.48550/arXiv.2601.16549A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks.
Abstract
The combination of multimodal Vision-Language Models (VLMs) and Large Language Models (LLMs) opens up new possibilities for medical classification. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities (binary and multiclass complexity) that contrasts traditional Machine Learning (ML) with contemporary transformer-based techniques. We evaluated three model classes for each task: Classical ML (LR, LightGBM, ResNet-50), Prompt-Based LLMs/VLMs (Gemini 2.5), and Fine-Tuned PEFT Models (LoRA-adapted Gemma3 variants). All experiments used consistent data splits and aligned metrics. According to our results, traditional machine learning (ML) models set a high standard by consistently achieving the best overall performance across most medical categorization tasks. This was especially true for structured text-based datasets, where the classical models performed exceptionally well. In stark contrast, the LoRA-tuned Gemma variants consistently showed the worst performance across all text and image experiments, failing to generalize from the minimal fine-tuning provided. However, the zero-shot LLM/VLM pipelines (Gemini 2.5) had mixed results; they performed poorly on text-based tasks, but demonstrated competitive performance on the multiclass image task, matching the classical ResNet-50 baseline. These results demonstrate that in many medical categorization scenarios, established machine learning models continue to be the most reliable option. The experiment suggests that foundation models are not universally superior and that the effectiveness of Parameter-Efficient Fine-Tuning (PEFT) is highly dependent on the adaptation strategy, as minimal fine-tuning proved detrimental in this study.
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PROBLEM
A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities (binary and multiclass comple...
METHOD
The combination of multimodal Vision-Language Models (VLMs) and Large Language Models (LLMs) opens up new possibilities for medical classification. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities (binary...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. According to our results, traditional machine learning (ML) models set a high standard by consistently achieving the best overall performance across most medical categorization tasks.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities (binary and multiclass complexity) that contrasts traditional Machine Learning (ML) with contemporary transformer-based techniques.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The combination of multimodal Vision-Language Models (VLMs) and Large Language Models (LLMs) opens up new possibilities for medical classification. This work offers a rigorous, unified benchmark by using four publicly available datasets covering text and image modalities (binary and multiclass complexity) that contrasts traditional Machine Learning (ML) with contemporary transformer-based techniques.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. According to our results, traditional machine learning (ML) models set a high standard by consistently achieving the best overall performance across most medical categorization tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark study evaluating the performance of classical ML versus transformer-based models in medical classification tasks.
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Medical AI
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