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Artificial Intelligence for Medical Diagnosis
Use Cases, Features, Challenges, and Costs
An ISO 13485-certified provider with three decades of AI experience, ScienceSoft delivers advanced medical diagnosis software that accurately and timely identifies diseases and helps reduce the risks of medical errors.
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AI for Medical Diagnosis in Brief
AI applications for medical diagnosis employ ML models to help detect diseases based on health records, medical images, vitals, and lab test results. Advanced solutions help diagnose patients faster, reduce treatment costs by up to 50%, and improve patient health outcomes by 40% [Harvard’s School of Public Health].
What Are Market Trends?
In 2023, the global market of artificial intelligence in medical diagnosis was estimated at $1.3 billion. By 2028, it is expected to reach $3.7 billion, growing at a CAGR of 23.2%. The main force driving the market growth is the desire of healthcare organizations to reduce staff workload and improve care quality.
As to the medical diagnosis market, 2023 witnessed an increased interest in AI, resulting in funding for AI projects. On the other hand, the public grew more skeptical about the safety of using the technology and called for closer regulatory review of AI apps for healthcare.
How AI for Medical Diagnosis Works
Architecture
According to ISO 13485 and IEC 62304 standards, the architecture of AI-based diagnostics software should be well-documented and planned with security in mind. ScienceSoft focuses on quality from the very start and designs secure architectures for each AI diagnostics project, following the requirements of the FDA, MDR, and other healthcare authorities.
Use cases
Health records
When patients have multiple conditions, it may be hard to differentiate the symptoms and make a correct diagnosis. AI helps analyze health records, find patterns, and define potential causes of a patient’s symptoms.
AI algorithms can interpret all types of medical images (CT, MRI, ultrasound, PET, SPECT, etc.). Using image segmentation, quantification, and other techniques, the software can find abnormal areas that may remain unseen by a radiologist or a physician.
Laboratory tests
ML algorithms identify correlations between abnormal lab test parameters, detect patterns that point to a certain disease, and generate a list of possible diagnoses.
Vitals
AI analyzes large amounts of vitals collected with the help of connected medical devices (e.g., continuous glucose monitors) and detects patterns and abnormalities.
Note: An AI-powered solution can handle several data types to identify unique disease patterns and diagnose patients correctly. For example, continuous Holter monitoring data can complement ECG scans to help detect heart conditions.
Sample features
When designing an AI system, our healthcare IT consultants help define what features will or will not work for your project. Here, we describe a sample feature set of an AI-based medical diagnostics solution.
Clinical data extraction
The software extracts information from diagnostic data sources (e.g., EHR records, lab tests) for further analysis.
Patterns identification
The software processes clinical data, applying diagnostic machine learning models (e.g., convolutional neural networks) to identify patients’ diseases or health conditions.
Diagnostic report generation
The software produces a report with key analysis findings, potential diagnoses, and highlighted abnormal test results or medical image areas. All patients’ data is available on the medical staff dashboard.
Patient risk analysis
After patient data assessment, the AI system calculates the potential risks and predicts patient outcomes based on the severity of abnormalities, patient age, other chronic conditions, and more.
Medical staff alerts
Healthcare specialists receive notifications if patients are diagnosed with a high-risk condition or are predicted to have drastic changes in their health state.
How ScienceSoft Implements AI-Powered Medical Software
Technology Behind Smart Diagnostics
How to Navigate the Challenges of AI-Powered Diagnostics
Challenge 1: The accuracy of the ML model may affect diagnostic efficiency.
Solution: During the discovery, ScienceSoft maps potential accuracy risks and plans ways to mitigate them. According to the plan, our data scientists ensure the high quality of data sets used for AI model training. They check if the data is accurate, up-to-date, and received from credible sources. If the data set ticks all the boxes, they choose the set that better represents the wide range of population to prevent biases. Whether they use ready-made or custom data sets, data scientists use dropout techniques and optimization algorithms to minimize the model’s error and conduct multiple tests to check if the AI model returns reliable diagnostic results.
Challenge 2: With time, the ML model may underperform.
Solution: As the underlying patterns in the data set evolve, the ML model should be continuously monitored to ensure its efficiency. At ScienceSoft, we are ready to prolong our services and track the deployed ML model using tools like Amazon SageMaker Model Monitor, etc. Our data scientists will analyze the input data and define metrics to track potential drifts to ensure the quality of diagnostics. If the deviation is detected, we will define the root cause, retrain the model, and promptly resolve data quality issues.
Challenge 3: The AI system processes sensitive data and may become a cyberattack target, posing non-compliance risks.
Solution: When developing healthcare software, ScienceSoft follows the secure-by-design approach and engages an in-house regulatory consultant early on. To prevent data theft risks and HIPAA/GDPR infringements, we ensure medical diagnosis software security using data access control and user authentication measures, data encryption, etc. Before FDA/CE submission, we conduct full security and HIPAA/GDPR pre-audit, to make sure PHI remains safe.
Costs of AI-Powered Software for Medical Diagnostics
Based on ScienceSoft’s experience, the costs of AI-powered software for medical diagnosis vary from $200,000 to $650,000+.
The former is common for a diagnosing solution that works with one type of data (e.g., medical images or unstructured health records) and uses an ML algorithm of moderate complexity. The latter is for an end-to-end system with a user side for several roles and complex algorithms processing various medical data types from various sources.
Want to get a tailored cost estimate for AI-powered diagnosis software?
Cost factors to consider
- The type of diagnostics and the complexity of ML algorithms.
- Costs of compiling or buying relevant data sets to create an ML model.
- The complexity of the data required for medical diagnostics: volume, type (structured or unstructured), number of data sources, etc.
- The number and complexity of software integrations (with one or several EHRs, medical imaging software, etc.).
- Security requirements and compliance-associated costs (e.g., for FDA/MDR submission, HIPAA/GDPR pre-audit).
- Deployment type (AI outputs are delivered in batches or in near real time).
- Infrastructure costs, including cloud storage, security tools, etc
Tap Into Our Decades-Long Healthcare and AI Experience
An ISO 13485-certified company, ScienceSoft knows all the ins and outs of delivering efficient, secure, and compliant AI-powered software for diagnostics.
Consulting on AI for medical diagnostics
Our healthcare IT consultants with 5–20 years of experience will plan your AI-powered software for diagnostics and help mitigate software safety, security, and compliance risks.
Implementation of AI for medical diagnostics
From shaping the idea to training ML models, developing a secure app, and submitting it for FDA or CE certification — ScienceSoft can do it all.
About ScienceSoft
ScienceSoft is an international IT consulting and software development company that has worked with AI and ML since its inception in 1989. In healthcare IT since 2005, ScienceSoft is experienced in delivering advanced medical software for diagnostics according to ISO 13485, ISO 27001, and ISO 9001 standards.