Diagnostic AI for Blood & Pathology

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statisticaal programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, AutoCAD, 2D drawing, 2D draftingPCB, Electronics, Arduino, Embedded Systems Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@devanshkhandekar

Hi,I’m anApplied ML/AI Engineer(6+ yoe) with hands-on experience building production-oriented solutions across medical AI,advanced computer vision,document intelligence,& RAG-based applications. -In medical AI,I built a fetal ultrasound biometry platform using PyTorch models for fetal-head segmentation,plane/view detection,image-quality assessment,abdominal & femur measurements,confidence fusion,& automated clinical reporting. -Proposed Solution: PDF/image/HL7/FHIR ingestion → PaddleOCR/DocTR + layout parsing → normalized schema (`test,value,unit,range,flag,confidence`) → deterministic abnormality checks → clinical-pattern reasoning → RAG-grounded explanation with citations. -I would keep deterministic medical rules separate from LLM reasoning,using the LLM for explanation & differential-support rather than uncontrolled diagnosis generation. -For patterns such as microcytic anaemia,inflammatory profiles,liver/kidney abnormalities,the system can surface likely differentials,supporting evidence,& next-step investigations with confidence thresholds. -Validation would cover field precision/recall/F1,numeric extraction accuracy,sensitivity/specificity,citation faithfulness,readability,& latency,targeting ≥95% extraction accuracy & <5s/report. -Deployment would include AWS-ready APIs,encryption,RBAC,audit trails,model/version logging,Docker scripts,evaluation suite,source code,& model hand-off with HIPAA/GDPR-oriented safeguards

@FreeLancers360

Hi there, Your project "Diagnostic AI for Blood & Pathology" instantly caught my attention — it's exactly the kind of work we love to take on. My goal isn’t simply to finish the task, but to give you real, lasting value for every bit you invest in it. From your brief I can see this involves ai, machine learning, data science — all areas we handle in-house. We specialise in Data Processing, Machine Learning (ML), Data Science, Tensorflow, which lines up directly with what you need. How we'd approach it: - Clarify the use-case, inputs and the exact output you expect - Build and integrate the model / automation pipeline - Evaluate accuracy and tune against real examples - Deploy with monitoring and a clear handover I’ll fold in your feedback fast and keep refining until the result feels exactly right to you. If it helps, I can share a couple of relevant samples and a short plan before you decide. Looking forward to it! Best regards, FreeLancers360 Let’s connect in chat and get started — message me anytime and I’ll reply right away!

@rakhshanz3

As an AI specialist with two decades of experience and a strong academic foundation, I am uniquely qualified for this project. My track record includes developing AI applications for document processing, data extraction and recommendation systems - all key elements for creating your medical diagnostic assistant. Moreover, the products I have developed align closely with the kind of HIPAA/GDPR-compliant, clinically accurate application with clear audit trails that you envision. I understand the importance of maintaining patient privacy, ensuring clinical accuracy and adhering to medical regulations. My familiarity with validation metrics assures you that I will meet the stringent requirements set forth in your acceptance criteria. In conclusion, my extensive experience in developing large-scale software solutions combined with my deep understanding of AI technologies makes me an ideal candidate for this significant undertaking. Let's work together to build a powerful, reliable and precise diagnostic tool that brings immense value to the world of medicine.

@ronakkataria1

Before architecture, the line that decides what this is allowed to be. Software that reads a pathology report and returns likely differential diagnoses and recommended next investigations is not a report reader, it is clinical decision support, and in most markets that puts it in medical device territory. An app that extracts values, compares them against reference ranges and flags what falls outside is on the other side of that line. The wording of the output is what moves it across. Worth deciding which side you want before any code, because it changes validation, labelling and what your hand-off package has to contain. On the technical side, 95% field extraction is reachable, but not from OCR alone. Lab layouts vary wildly and the same analyte appears as Hb, HGB or Haemoglobin with units that differ by lab, so extraction needs a layout-aware pass plus an analyte dictionary with unit normalisation, and any value failing a plausibility check gets flagged rather than guessed. Reference ranges are age and sex dependent and differ per lab, so they should come from the report itself wherever it prints them. HL7 and FHIR inputs are the easy path and worth wiring first, since they arrive structured. Is this aimed at clinicians or at patients? That changes both the wording and the regulatory position. Ronak

@Ashoksahu001

As a seasoned developer with over a decade of experience, I can leverage my extensive skill set to address your project's foundational needs. My expertise spans React.js, Angular, PHP, and more—a toolkit ideal for developing the lightweight cross-platform front-end you'll require. Additionally, my proficiency in Data Integration and Data Processing will prove invaluable in ensuring the efficient extraction of crucial field data with the aim of exceeding the project's ambitious 95% accuracy benchmark on your mixed-format reports. But where I truly stand out is in my proven ability to build custom technology solutions in sensitive and highly-regulated sectors. Notably, I have successfully delivered Healthcare Systems that adhere to stringent regulatory standards such as HIPAA and GDPR. Consequently, Privacy is not just a buzzword to me; it's engrained in my coding philosophy ensuring that your patients' data will be protected at every step. Moreover, seamless user experience is crucial. Here my work with flutter and react native come into play. My expertise ensures reduced end-to-end latency while maintaining clean integration with OCR and other data sources. My track record shows that I specialize in hitting the perfect intersection between utility and simplicity.

@aakaashberi

For clinical accuracy and audit trails, the abnormal flags should not come from a model. Extracted values are checked against reference ranges by deterministic rules, and AI is used only to read the report and explain the result in plain language. That split is what makes every prediction traceable. Architecture: upload a PDF, photo or HL7/FHIR payload -> OCR (AWS Textract) plus a parser that maps each test to a FHIR Observation with value, unit and range -> a rules engine that flags abnormal values and pattern profiles (anaemia, inflammatory markers) -> an LLM that writes an 8th-grade explanation citing the rule and guideline behind each flag -> an audit log storing input, extracted values, rule hits and output per report. Web front end first (React), FastAPI backend, encrypted storage on your AWS account. Validation: field-level precision/recall on your blind set, latency tracked per stage. You get a first working version in 4 days (PDF upload, extraction, flags and explanation for CBC reports); the full scope takes about 4-6 weeks. INR 12,500 covers this first phase. Positioned as decision support; the clinical sign-off and regulatory filing stay with your team. Past client work is under NDA, so the day-4 demo is my proof. How many sample reports can you share, and which lab formats come first? Aakaash

@kushalmittal2001

Hi, this is squarely what I build: document or photo in, OCR and structured extraction, then an LLM that explains the result in plain language. I have 6 apps live on the App Store, including scan-and-analyse products that run OCR + AI pipelines in production. How I'd build the MVP: 1. Intake: PDF, image or phone photo (with OCR that handles skewed photos), plus an HL7 / FHIR parser for structured payloads 2. Extraction to a strict schema (test name, value, unit, reference range) with unit normalisation. The numbers come from deterministic parsing and validation, never from the model guessing 3. Rule-based flagging against reference ranges (age / sex aware where the report provides it), so every "high / low" is traceable 4. The LLM only interprets what was flagged: plain-language explanation, recognised patterns (e.g. anaemia profile, inflammatory markers), possible differentials and suggested next investigations, with the evidence cited for each point 5. Safety: every output framed as decision support for a clinician, clear disclaimers, and no patient data stored unless you want it (with encryption and an audit log if you do) Deliverable: a working web app + API you can plug into your own front end, with tests on sample reports. Timeline: about 2 weeks for the MVP. Do you have a set of sample reports (anonymised) from the labs you'll support first? Lab formats vary a lot, and that decides how much OCR tuning is needed. Kushal

@usmansiddiqui001

Hello, Could you provide a few de-identified sample reports and the target reference ranges/clinical guidelines you want the system to use? 1. **Architecture:** Python + FastAPI backend, OCR/document parsing layer, structured clinical data model, ML/NLP pipeline, and lightweight web frontend. AWS-ready deployment using Docker. 2. **Input Processing:** Support PDFs, images/photos, and HL7/FHIR through dedicated ingestion adapters. OCR will handle scanned reports while structured feeds bypass OCR where possible. 3. **Extraction Pipeline:** Detect test names, values, units, reference ranges, dates, and patient/report metadata, then normalize them into a consistent schema. 4. **Clinical Reasoning:** Combine deterministic reference-range/rule validation with ML/LLM-based pattern interpretation rather than relying solely on generative output. 6. **Validation:** Establish separate extraction, classification, and explanation test sets. Measure precision, recall, F1, field-level accuracy, false-positive/negative rates, latency, and calibration/confidence. 7. **Safety:** Implement human-review workflows, uncertainty thresholds, prohibited/unsafe output handling, PHI minimization, encryption, access controls, logging, and configurable data-retention policies. HIPAA/GDPR requirements should be validated with your compliance/legal team. Regards Usman

@pandaymano8

As an experienced Full-Stack Developer with a specialization in AI and Automation, I am well-versed in the precise skills needed to build your Diagnostic AI for Blood & Pathology project. My proficiency in Machine Learning and Natural Language Processing using top frameworks such as PyTorch and TensorFlow make me a perfect fit for this task. Throughout my career, I have built numerous systems that involve clean data extraction, just like the one necessary for this project. My thorough understanding of OCR and direct-feed parsing will ensure that your platform effectively recognize, analyze, and interpret all types of medical reports with a field-extraction accuracy of 95% or above. Moreover, with my focus on HIPAA/GDPR-compliant solutions, you can be assured about privacy and regulatory safeguards. I can bring your vision to life by combining the robustness of my back-end pipeline with a lightweight cross-platform front-end design for web-first accessibility and future mobile integration. At the end of the project, you will have the complete hand-off package ready for deployment on your AWS account. Let's build the future of medical diagnostics together!

@indreshpnk

Hello, I understand you need an AI-driven medical diagnostic assistant that can process blood/pathology reports, extract results, identify abnormalities, and provide evidence-based clinical insights. The goal is a secure, accurate, and production-ready solution with clear audit trails. Here’s what I can provide: * OCR/PDF/HL7/FHIR data extraction with structured report processing. * ML/NLP pipeline for reference-range checks, abnormality detection, and explainable insights. * AWS-ready architecture with privacy controls, validation metrics, audit logging, and scalable deployment. I bring over 4+ years of experience in Python, ML/NLP, data processing, REST APIs, and AWS, with a strong focus on reliable and scalable applications. Just to clarify a few things: * Do you already have an annotated dataset of pathology reports, or should dataset preparation and annotation be included? * Which HL7/FHIR versions and clinical reference sources should the system support? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@VirasatSolutions

Hi! I can build a secure, production-ready AI medical report interpretation platform designed around accurate extraction, transparent clinical reasoning, privacy, and auditability. ++ What I’ll deliver: → PDF/image/photo OCR and HL7/FHIR ingestion pipeline → Robust extraction and normalization of lab values, units, reference ranges, and patient metadata → Python-based ML/NLP architecture using PyTorch/TensorFlow with rule-based clinical validation → Abnormality detection and pattern recognition for findings such as anaemia and inflammatory profiles → Evidence-grounded explanations with citations and 8th-grade readability → Differential-diagnosis and next-investigation suggestions clearly presented as clinical decision support, not autonomous diagnosis → Complete prediction/audit trail including model version, inputs, outputs, confidence, and evidence → HIPAA/GDPR-focused security, encryption, access controls, data minimization, and configurable retention → AWS-ready deployment using Docker and infrastructure/deployment scripts → Cross-platform web interface with mobile-ready architecture → Blind-set validation targeting ≥95% field extraction accuracy, plus precision, recall, F1, calibration, latency, and clinical-safety metrics → Source code, model weights, documentation, and deployment hand-off Regards, Abhay

@Maanie786

Hello, I understand you're developing an AI application for medical diagnostics, focusing on accurate data extraction and interpretation from various report formats. This is a critical area where precision and clarity are paramount. My expertise in machine learning and natural language processing aligns perfectly with your needs. I have worked on projects that required robust OCR capabilities and the implementation of ML pipelines using TensorFlow and PyTorch, ensuring high accuracy in field extraction. I can provide a detailed architecture plan, dataset strategy, and validation metrics tailored to your requirements. Regulatory safeguards will be integrated to comply with HIPAA and GDPR standards, ensuring privacy and security. Starting a conversation would cost you nothing, and I’d be happy to offer a free consultation to discuss your project further. Regards, Maanie

@michela542

Hi, I've built several apps and systems for different clients over the years. ➤ The hardest part of a system like this is getting the extraction right. Reports come in as scans, photos, or structured HL7/FHIR data, and each one needs to read cleanly before any interpretation can be trusted. If the numbers are off, everything downstream is wrong too. ✅ I ran into this exact problem on a similar project. OCR kept struggling with different report layouts and photo quality, so I added a validation step that checked every extracted value against the expected units and reference range for that test. Anything that looked off got flagged for review instead of passing through silently. HL7/FHIR data went through its own separate parsing path, so it never got mixed up with the OCR logic. ✅ That approach worked well. Extraction stayed accurate even across messy, mixed formats, shaky readings got caught early, and the whole thing still ran fast despite the extra checks. I can build the full pipeline with the core functionality, OCR/direct-feed parsing, the ML/NLP interpretation layer, and the web front-end. Best regards, Michel.