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Custom Medical Diagnosis Software
Features, Development Steps, & Costs
In custom healthcare software development since 2005, ScienceSoft builds rule-based and AI-powered solutions that help healthcare professionals increase the accuracy of diagnostic decisions and minimize errors.
Contributors
Healthcare IT Consultant, ScienceSoft
Senior Solution & Integration Architect, ScienceSoft
Medical diagnosis software processes healthcare data like lab test results, medical images, clinical guidelines, and medical histories to suggest possible diagnoses, detect abnormalities in medical images with greater accuracy, and automatically interpret lab test results.
According to Data Bridge, the global medical diagnosis software market is projected to reach $59.83 billion by 2032. The market is driven by the growing demand for high-quality medical care.
Custom medical diagnosis software is a popular choice among organizations that want to enable diagnostic assistance based on a unique combination of data sources. It also allows organizations to benefit from complex ML/AI-driven capabilities, e.g., medical image analysis and data extraction from unstructured clinical texts.
- Implementation time: 6 to 18+ months.
- Common integrations: EHR/EMR, LIMS/LIS, RIS, PACS, remote patient monitoring software (RPM).
- Costs: $100,000–$1,200,000+, depending on the solution's complexity. Use our free online calculator and get a tailored quote from our consultants.
Popular Features of Medical Diagnosis Software
The output of medical diagnosis software can be AI-driven and rule-based. AI-powered solutions typically employ deep learning, natural language processing (NLP), and other models. In contrast, rule-based solutions rely on predefined thresholds to compare healthcare data and make decisions based on established criteria.
Comprehensive healthcare data analysis
The system can correlate a patient’s symptoms with information in clinical guidelines and EHR records (e.g., medical history, lab test results, and imaging study results) to generate a list of possible diagnoses and personalized treatment options. When powered by machine learning models (e.g., convolutional neural networks), the system can analyze disparate clinical data, identify hidden patterns, and suggest potential underlying causes of a patient’s symptoms.
The software can analyze medical images from different medical fields, for example, radiology (e.g., MRIs, CT scans, PET), hematology (e.g., peripheral blood smear images), cardiology (e.g., ECGs), and ophthalmology (e.g., retinal images). AI/ML models can help detect abnormalities (tumors, fractures, lesions, organ malfunctions) that may escape the attention of healthcare professionals. The system can also help determine the size, location, shape, and tissue type of abnormalities. Some solutions can also analyze videos (e.g., real-time detection of polyps during endoscopy).
Predictive analysis
Medical diagnosis software can predict risks, health outcomes, disease progression, and treatment responses by analyzing different types of patient data. This data can include current and historical clinical data, demographics, genomic profiles, longitudinal data, vitals from remote patient monitoring software, and other information. With such software, clinicians can anticipate potential complications and personalize treatment plans accordingly.
Interpretation of lab test results
Medical diagnosis solutions can automatically interpret test results by comparing them against the predefined thresholds and highlighting abnormal values. Such solutions can be integrated with diagnostic medical devices (e.g., portable blood analyzers) and LIS or LIMS. They can also be programmed to validate a test’s quality before interpreting it.
3D labeling
The software can transform medical images into 3D models, giving clinicians a detailed view of anatomical structures. These capabilities enhance diagnostic accuracy, aid in complex case analysis, and support surgical planning.
Diagnostic coding
The system can extract the required clinical information from medical records, diagnostic images, clinical notes, and reports and then pinpoint an appropriate diagnostic code in line with standardized coding systems such as ICD (International Classification of Diseases) or CPT (Current Procedural Terminology).
Automated patient triage
Using clinical scoring algorithms or triage protocols (e.g., Emergency Severity Index [ESI], Manchester Triage System [MTS]) as well as real-time and historical data, the software can compare the severity of patients’ symptoms and conditions and automatically group them by the urgency of care required. The system can also suggest the most appropriate next steps based on the patient’s condition, risk factors, and clinical guidelines.
Diagnostics data analysis
A BI and analytics component can generate reports on potential diagnoses, identified abnormalities, medication interactions, and other diagnostic aspects to streamline decision-making. Reports can also feature info on health outcomes, treatment efficacy, and other metrics to enhance the quality of care in the future.
Medication interaction
The system can identify and flag medication-related issues (e.g., improper drug interactions, allergies, adverse events, duplicate therapy) so that clinicians can consider them when diagnosing.
Acoustic data analysis
Medical diagnosis software can analyze sounds from different fields, including cardiology (e.g., heartbeat sounds indicative of arrhythmia); pulmonology (e.g., wheezes typical for pneumonia), and neurology (e.g., voice pitch and rhythm that can signal Parkinson’s disease or aphasia).
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Key Integrations for Medical Diagnosis Software
Below, our healthcare solution architects outline key integrations for a medical diagnosis system that provides output based on end-to-end patient data, including demographics, medical history, social determinants of health (SDOH), and more. The described system is integrated into the clinical workflows — it sends analysis results and notifications directly to the EHR. Depending on the use case, medical diagnosis software can be integrated with other systems to share analysis results.
To get a better idea of how such solutions work, you can explore a sample architecture of custom medical diagnosis software.
- EMR/EHR – to consolidate patients’ medical data for analysis; to compare findings against clinical guidelines and provide personalized diagnostic and treatment recommendations.
- Laboratory information system (LIS) or laboratory information management system (LIMS) — to flag abnormal findings in lab test results and suggest possible diagnoses accordingly.
- Radiology information system (RIS) + picture archiving and communication system (PACS) — to enable analysis of medical images; to consider image study reports when generating a list of potential diagnoses.
- Remote patient monitoring software (RPM) — to identify patterns in real-time health data from wearables and medical devices.
- Pharmacy systems — to analyze a patient’s medication history for personalizing medication selection and dosage calculation.
- Patient-facing software (e.g., patient portals, telehealth platforms) — to get timely updates on patient treatment progress and alert clinicians on abnormal findings.
How to Develop Custom Medical Diagnosis Software?
Custom medical diagnosis software development is needed to accommodate unique diagnostic needs and integrate the solution with multiple existing systems. In custom healthcare software development since 2005, ScienceSoft outlines key steps for successful medical diagnosis software implementation.
1.
Business analysis and requirement engineering
At this stage, healthcare consultants collaborate with stakeholders to identify functional and non-functional software requirements. For instance, they find out what diagnostic capabilities the software should have or how scalable it should be. The specialists also need to understand what software the solution should be integrated with, the types and number of user roles, and the specifics of collaboration among different user roles. Based on the diagnostic capabilities for future software, experts determine whether the software will be ML/AI-powered or rule-based.
If the software is intended for commercial distribution rather than internal use, the specialists analyze the specifics of the targeted institutions, perform competitive market analysis, and identify features that will help the product company outperform its competitors.
Healthcare consultants also outline the regulatory requirements the software should comply with (e.g., HIPAA, GDPR, PIPEDA, PDPL for personal data protection).
Some medical diagnosis software can qualify as Software as a Medical Device (SaMD). For example, this can be medical image diagnosis software that identifies abnormalities and suggests possible diagnoses. Such software is subject to clearance by regulatory bodies (e.g., FDA in the US) and must adhere to IEC 62304:2006/Amd 1:2015, ISO 1497, and ISO 13485 standards. In some regions, these international standards can be part of the assessment process under local requirements, e.g., MDR and IVDR in the EU. These standards are crucial to ensure that the software is developed, tested, and maintained in a manner that safeguards patient health.
2.
ML/AI model development
Data scientists decide on an optimal approach to ML/AI development, for example, they need to decide whether to use an open-source model or build a custom one. Pre-built LLM models can work well for extracting clinical information like symptoms from unstructured texts in medical records. Medical image analysis is more likely to require custom development. To build such a model, data scientists perform exploratory data analysis, gather and clean data, train the model, test its accuracy, and refine it if needed.
When developing an AI model we focus not only on its accuracy but also on optimizing the total cost of ownership (ToC) of the future solution. For example, to enable a solution that provides diagnosis suggestions based on medical image analysis, we can use a multi-model approach — have a computer vision model to detect abnormalities in medical images and an LLM model to scan relevant medical history records and knowledge bases of symptoms and conditions. Each model will process only dedicated datasets, while one generalized model would handle all the available data and increase computational overhead.
3.
Technical design
Solution architects plan the solution's integration with the required back-office systems (e.g., PACS and RIS for medical image analysis). For example, they may need to create an API development plan and outline ways to ensure adherence to the relevant data exchange standards (e.g., DICOM, HL7, FHIR). The experts decide on software components (e.g., data storage, data processing, and analytics engine) and choose techs to power the designed workflows at an optimal cost-to-benefit ratio.
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4.
UX and UI design
UI and UX designers create software elements and paths that ensure the solution can be easily incorporated into the clinical workflow. For example, they can embed a diagnostic solution window into the EHR or LIS, so that clinicians can access test results, interpretations, and recommendations without leaving their primary system. One of the best UI design practices that helps shorten the learning curve is to audit the software the organization already has in use and transfer familiar buttons and widgets to the new solution.
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5.
Testing and development
When it suits the project’s specific needs, testing and development teams work in parallel. Such an approach streamlines cross-team collaboration and helps fix issues as soon as they arise. Developers usually proactively look for ways to optimize project costs while building high-quality software. They can reuse services and ready-made components of reputable providers (e.g., Azure, Amazon, Google Cloud), use feasible QA automation, and implement CI/CD pipelines. At ScienceSoft’s projects, such best practices have helped us cut development costs by up to 78%.
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5.
Deployment and support
The specialists integrate medical diagnosis software with the required systems, monitor its performance, and fix the remaining flaws, if any. One of the major deliverables of this stage is comprehensive software documentation, including API usage and maintenance guides, as well as user training materials.
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How Much Does It Cost to Develop Custom Medical Diagnosis Software?
The cost of custom medical diagnosis software development may range between $100,000 and $1,200,000+. The exact figure will depend on the number and complexity of capabilities, the type of features (rule-based or AI-powered), the sophistication of required integrations, the quality and volume of data needed for AI model training, and more.
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Basic solution |
Medium-complexity solution |
Advanced solution |
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Scope and complexity of capabilities
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Rule-based mechanisms that rely on set thresholds and clinical guidelines (e.g., symptom checkers). |
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Level of data entry automation
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Partial automation (e.g., prefilled demographics and clinical history data requiring manual entry of other parameters) |
Automated data ingestion from the integrated systems (e.g., lab test results from LIS). |
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Diversity of data for diagnostics
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2–3 types of data, e.g., lab test results and symptoms. |
3–5 types of data, e.g., demographics, treatment history, imaging study data. |
End-to-end patient data, including medical images, results of past imaging studies, social determinants of health (SDOH). |
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Integrations
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1 system (e.g., EHR, LIMS/LIS) |
2–3 systems (e.g., EHR and LIS/LIMS; EHR, RIS, and PACS) |
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Volume and quality of data for AI model training
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Higher data quality is associated with less effort spent on data cleansing and other preparation actions. |
Inapplicable due to the rule-based nature of the features. |
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Personalized treatment recommendations
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Integration into the clinical workflow
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E.g., to send notifications to patients and clinicians, set tasks. |
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BI and reporting
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Costs
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$100,000– $200,000 |
$200,000– $500,000 |
$200,000– $1,200,000+ |
Why Develop Your Medical Diagnosis Software With ScienceSoft?
- Since 2005 in healthcare IT with 150+ successful projects in the domain.
- Extensive experience in ensuring compliance with regulatory requirements, including those for data protection (e.g., HIPAA, GDPR) and SaMD software (e.g., MDR, IEC 62304:2006/Amd 1:2015, ISO 1497, and ISO 13485).
- Since 1989 in AI and machine learning
- Since 2013 in image analysis software development with the expertise acknowledged in 2022 market leader research by MarketsandMarkets and Coherent Market Insights.
- Proficiency in healthcare data exchange standards, including HL7, DICOM, ASTM, and more.
- ISO 9001-certified quality management and ISO 27001-certified information security management.
- ISO 13485-certified to deliver regulatory-compliant SaMD software.
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