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| Pricing | Free | Free |
| Capabilities | 7 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Accepts unstructured clinical presentation data (chief complaint, history of present illness, physical exam findings, lab results) and generates ranked differential diagnosis lists using LLM reasoning with embedded medical knowledge. The system processes free-text clinical narratives through prompt engineering that enforces structured diagnostic reasoning, prioritizing conditions by epidemiological likelihood and clinical relevance rather than simple keyword matching. Architecture relies on few-shot prompting with real clinical case examples to guide the LLM toward clinically sound differential generation.
Unique: Uses transparent LLM reasoning chains to generate differentials with explicit clinical logic (e.g., 'fever + rash + meningismus → meningitis high on differential because classic triad'), rather than black-box ML models or simple rule engines. Emphasizes rare disease coverage by leveraging LLM's broad training data on uncommon conditions, addressing a gap in traditional decision support tools optimized for common presentations.
vs alternatives: Provides free, transparent reasoning for rare disease consideration vs. proprietary tools like UpToDate or Isabel that require subscriptions and use opaque algorithms; more accessible than specialist consultation but less validated than peer-reviewed diagnostic criteria.
For each differential diagnosis suggestion, the system generates a natural-language explanation of the clinical logic connecting the patient's presentation to the suggested condition. This works by prompting the LLM to explicitly state which clinical features (symptoms, signs, labs) support each diagnosis and how they align with epidemiological or pathophysiological patterns. The explanation layer enables clinicians to verify reasoning rather than blindly accepting suggestions, functioning as a transparency mechanism for AI-assisted decision-making.
Unique: Explicitly structures LLM output to separate diagnostic suggestions from reasoning explanations, forcing the model to articulate the clinical logic rather than just listing conditions. This transparency-first approach contrasts with black-box ML models and even some LLM-based tools that provide suggestions without reasoning chains.
vs alternatives: More transparent than traditional ML-based decision support (e.g., machine learning models trained on EHR data) but less rigorous than peer-reviewed diagnostic criteria or clinical guidelines, which have explicit evidence hierarchies.
Leverages the broad training data of large language models to surface rare diagnoses and complex condition combinations that might be overlooked in time-pressured clinical environments. The system works by encoding the patient presentation and allowing the LLM to generate differentials across its entire knowledge base without filtering to 'common' diagnoses. This is particularly effective for zebra cases, atypical presentations of common diseases, and rare genetic or infectious conditions where clinician familiarity is low.
Unique: Explicitly leverages the broad training data of LLMs to surface rare diagnoses without filtering to 'common' conditions, addressing a known gap in traditional decision support tools that optimize for high-prevalence diagnoses. This is a knowledge-breadth advantage rather than a reasoning sophistication advantage.
vs alternatives: Broader rare disease coverage than traditional decision support tools (UpToDate, Isabel) which optimize for common diagnoses; less validated than specialist consultation but more accessible and faster.
Accepts free-text clinical narratives (chief complaint, history of present illness, physical exam notes, lab result descriptions) and processes them through the LLM to extract and normalize clinical information into a structured format suitable for diagnostic reasoning. The system uses prompt engineering to guide the LLM to identify key clinical features, temporal relationships, and severity indicators from unstructured text. This enables clinicians to input data in their natural documentation style without requiring structured data entry.
Unique: Uses LLM-based processing rather than traditional NLP pipelines (regex, named entity recognition, rule-based extraction) to handle the semantic complexity and variability of clinical narratives. This approach is more flexible than rule-based systems but less validated than specialized clinical NLP models trained on annotated clinical corpora.
vs alternatives: More flexible than rule-based clinical NLP for handling diverse documentation styles; less validated and potentially less accurate than specialized clinical NLP models (e.g., cTAKES, MedSpaCy) trained on annotated clinical text.
Provides diagnostic support at the moment of clinical decision-making through a web interface that requires manual input of clinical data rather than automatic EHR integration. The system is designed for rapid access and minimal setup—clinicians can open the tool, paste or type clinical information, and receive differential diagnoses within seconds. This architecture trades integration friction for deployment simplicity and avoids complex EHR API dependencies.
Unique: Deliberately avoids EHR integration to prioritize deployment speed and accessibility across diverse healthcare settings. This is a trade-off decision: simpler deployment and broader accessibility vs. higher friction and manual data entry. Most competing tools (UpToDate, Isabel) require EHR integration or at least structured data input.
vs alternatives: Faster to deploy and more accessible than EHR-integrated tools; less integrated into clinical workflow and more prone to data entry errors than tools with native EHR connectors.
Provides full access to differential diagnosis generation and clinical reasoning explanations without requiring payment, subscription, or institutional licensing. The business model removes financial barriers to adoption, allowing individual clinicians to experiment with AI-assisted diagnostics regardless of their institution's budget or purchasing decisions. This is implemented through a freemium model where core diagnostic functionality is available without payment.
Unique: Removes financial barriers to adoption by offering core diagnostic functionality for free, contrasting with subscription-based competitors (UpToDate, Isabel) that require institutional or individual payment. This is a business model and accessibility choice rather than a technical differentiation.
vs alternatives: More accessible than subscription-based diagnostic tools; sustainability and long-term viability unclear compared to established paid tools with proven business models.
Accepts clinical data across multiple organ systems and integrates them into a unified differential diagnosis that considers multi-system involvement and systemic conditions. The system uses LLM reasoning to identify patterns that span multiple systems (e.g., fever + rash + joint pain + eye inflammation → systemic inflammatory condition) rather than generating separate differentials for each system. This enables consideration of connective tissue diseases, vasculitides, infections, and other conditions that present with multi-system involvement.
Unique: Explicitly integrates clinical data across multiple organ systems to identify systemic conditions and multi-system patterns, rather than generating separate differentials for each system. This requires LLM reasoning that can hold multiple data streams in context and identify cross-system relationships.
vs alternatives: More holistic than single-system decision support tools; less validated than specialist consultation for complex multi-system cases but more accessible and faster.
Enables evaluation of AI systems' ability to perform chained mathematical operations (addition, subtraction, multiplication, division, comparisons) across both structured tables and unstructured text extracted from SEC filings. The dataset provides ground-truth question-answer pairs where answers require synthesizing data from multiple locations within earnings reports and applying sequential arithmetic operations, testing whether models can decompose complex financial queries into discrete computational steps.
Unique: Combines real SEC filing documents (not synthetic) with crowdsourced questions requiring multi-step arithmetic, creating a hybrid dataset that tests both domain knowledge extraction and quantitative reasoning in a single evaluation task. Unlike generic math word problems, answers require locating figures within 10+ page documents first.
vs alternatives: More challenging than DROP or SVAMP because it requires financial domain knowledge AND document retrieval before arithmetic, whereas generic math benchmarks assume figures are already extracted
Assesses whether AI systems understand financial terminology, accounting concepts, and domain-specific metrics by requiring them to answer questions about real earnings reports from S&P 500 companies. The dataset tests recognition of financial line items (revenue, COGS, operating expenses, net income), ability to distinguish between different financial statements (income statement vs balance sheet), and understanding of financial ratios and metrics without explicit instruction on their definitions.
Unique: Uses authentic SEC filings rather than synthetic financial data, exposing models to real-world accounting variations, footnote complexity, and the actual structure of professional financial documents. This tests transfer learning from general text to specialized domain without domain-specific pretraining.
vs alternatives: More authentic than synthetic financial QA datasets because it uses real earnings reports with their inherent complexity, but narrower than general financial knowledge benchmarks because it focuses only on historical data interpretation
FinQA scores higher at 60/100 vs glass.health at 43/100.
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Enables evaluation of AI systems' ability to extract numerical data from both structured HTML/text tables and unstructured prose within the same document, then reason over the extracted values. The dataset contains questions where relevant data appears in different formats — some figures are in formatted tables with clear row/column headers, while others are embedded in narrative text or footnotes — requiring robust parsing and entity linking before computation can occur.
Unique: Combines structured table data with unstructured narrative in the same evaluation, forcing systems to handle format heterogeneity and resolve references across different data representations. Most table QA datasets use clean, isolated tables; this tests real-world document complexity.
vs alternatives: More realistic than isolated table QA benchmarks (like SQA or WikiTableQuestions) because it requires handling narrative context and format mixing, but simpler than full document understanding because tables are already in text format (no OCR needed)
Provides a curated, crowdsourced-annotated dataset of 8,281 question-answer pairs with multi-step reasoning requirements, enabling systematic evaluation of AI systems on financial numerical reasoning. The dataset includes quality control mechanisms through crowdworker annotation, answer validation against ground truth, and coverage across diverse financial metrics and company types within the S&P 500, creating a reproducible evaluation standard for the financial AI community.
Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.
vs alternatives: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning
Assesses AI systems' ability to perform multi-hop reasoning by requiring them to locate and combine information from different sections of earnings reports. Questions may require finding a figure in the income statement, then locating a related metric in the balance sheet, then performing arithmetic across both — testing whether models can maintain context across document boundaries and understand relationships between different financial statement sections.
Unique: Embeds multi-hop reasoning requirements within authentic financial documents where hops correspond to real relationships between financial statement sections, rather than synthetic reasoning chains. This tests whether models understand domain structure, not just generic multi-hop patterns.
vs alternatives: More realistic than synthetic multi-hop datasets (HotpotQA, 2WikiMultiHopQA) because reasoning hops follow actual financial relationships, but less controlled because document structure varies and reasoning paths are implicit rather than explicitly annotated
Enables evaluation of whether AI systems can identify which arithmetic operations (addition, subtraction, multiplication, division, comparison) are required to answer financial questions, then execute them correctly. The dataset implicitly tests operation selection — a question asking 'what is the profit margin' requires division (net income / revenue), while 'what is total assets' requires addition — forcing models to understand financial semantics before applying math.
Unique: Embeds arithmetic operation selection within financial domain context, requiring models to understand that 'margin' semantically maps to division and 'total' maps to addition. This tests semantic grounding of operations, not just arithmetic execution.
vs alternatives: More semantically grounded than generic math word problem datasets because operation selection is implicit in financial terminology, but less explicit than datasets with annotated operation types because operations must be inferred
Provides evaluation capability for AI systems to compare financial metrics across multiple S&P 500 companies or aggregate metrics across different time periods within the same company's earnings reports. While individual questions reference single documents, the dataset structure enables evaluation of systems that can retrieve and compare relevant companies, requiring understanding of which metrics are comparable across entities and how to normalize for company size or accounting differences.
Unique: Provides a foundation for evaluating cross-company financial comparison by including diverse S&P 500 companies with different business models and scales, enabling assessment of whether systems can normalize and compare metrics appropriately. Most financial QA datasets focus on single-document questions.
vs alternatives: Enables cross-company evaluation unlike single-document QA datasets, but requires external retrieval and comparison logic because the dataset itself contains only single-document questions