DiagnomIQ
                     DIAGNOMIQ
AI-Powered Clinical Decision Support Platform
CASE STUDY
Supporting structured cancer-case assessment, multidisciplinary
workflows, and clinician-led validation.
Softnotions Technologies
Healthcare AI • Data • Digital Engineering
Source: Softnotions DiagnomIQ case study page
https://softnotions.com/case-studies/diagnomiq/
DiagnomIQ Case Study | Softnotions Technologies Page 1
1. Executive Overview
DiagnomIQ is an AI-powered Clinical Decision Support Platform developed by Softnotions. It is
designed to help healthcare professionals generate treatment reports, analyse clinical data, and
support assessment of complex cancer cases. The platform brings clinical information, specialized AI
analysis, multiple clinical perspectives, consensus evaluation, and multidisciplinary team (MDT)
workflows into a structured digital environment.
Platform DiagnomIQ
Category Healthcare AI / Clinical Decision Support
Primary focus Complex cancer-case assessment and MDT workflows
Core approach Multi-agent, multi-model AI with consensus evaluation
Human oversight Clinician review and validation
Access model Web and mobile workflows for hospitals, doctors and public users
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2. Business Challenges
Managing Complex Clinical Information
Cancer cases can involve pathology, radiology, laboratory results, treatment history, medical
records and disease progression. Bringing these sources together for a complete case assessment
can be complex and time-consuming.
Bringing Together Multiple Clinical Perspectives
MDT assessments involve specialists from different clinical areas. Reviewing the same case from
different perspectives and consolidating their findings into a structured assessment can be
challenging.
Supporting Structured Clinical Assessment
Clinical teams need to review information from multiple perspectives, identify agreement or
difference, and organize findings clearly to support MDT discussions.
Need for an Integrated Digital Workflow
The case study describes a need to connect clinical information, AI-assisted analysis, multiple clinical
perspectives, consensus evaluation and MDT reporting within one workflow.
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3. Proposed Solution
DiagnomIQ provides a structured AI-assisted approach to managing complex cancer cases. It brings
relevant clinical information together, analyzes the case from multiple clinical perspectives, and
supports healthcare professionals in developing a comprehensive assessment.
Structured Case Assessment
Pathology, radiology, laboratory results, treatment history, disease progression, medical documents
and other relevant information can be organized in one structured workspace. Specialized AI agents
and models analyze the information to identify relevant findings, relationships and areas that may
need further review.
Intelligent MDT Report Generation
The platform compares analysis from different clinical perspectives and AI models to identify
consistent findings, differences and areas of uncertainty. Its Consensus Engine evaluates findings
and can trigger additional analysis when significant differences or inconsistencies are identified.
Clinician-in-the-Loop
DiagnomIQ is designed to support clinical expertise rather than replace it. AI-generated findings and
reports can be reviewed and validated by qualified healthcare professionals, while final clinical
decisions remain with the appropriate clinician or MDT team.
Integrated Clinical Workflow
The workflow includes Clinical Voice Scribe, structured case management, document management,
configurable MDT reports, role-based access, hospital and doctor workflows, and public-user
workflows.
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4. Key Features
• AI-Assisted MDT Report Generation
Uses specialized AI agents and multiple AI models to analyze complex cancer cases from relevant clinical perspectives.
• Multi-Agent Clinical AI
Supports perspectives including Hematopathology, Oncology, Radiology and Supportive Care.
• Multi-Model AI Intelligence
Compares outputs from different models for similarities, differences, conflicts and areas requiring further evaluation.
• Consensus Engine
Evaluates findings, identifies disagreements or inconsistencies, and can trigger additional analysis before report generation.
• Clinical Voice Scribe
Enables clinicians to capture clinical information using voice and reduce repetitive manual data entry.
• Comprehensive Clinical Data Capture
Organizes medical history, pathology, radiology, laboratory results, treatment history, disease progression and supporting
documents.
• Clinician Review & Validation
Allows qualified healthcare professionals to review and validate AI-assisted findings and reports.
• Hospital-Specific MDT Workflows
Supports configurable workflows, roles, permissions and hospital-specific MDT report templates.
• Doctor & Patient Management
Supports management of doctors, patients, clinical cases, roles and permissions.
• Web & Mobile Access
Provides workflows for hospitals, doctors and public users across web and mobile.
• Secure Role-Based Access
Uses role-based permissions to help control access according to responsibilities.
• Healthcare System Integration
Designed to work alongside existing hospital applications and healthcare systems.
• Configurable Reporting
Supports configurable report structures and preferred MDT report formats.
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5. Advantages & Workflow Value
• Comprehensive multidisciplinary assessment by bringing clinical information and multiple
perspectives into a structured workflow.
• AI-assisted comparative analysis across different agents and models to surface similarities,
differences and areas for further evaluation.
• Reduced documentation effort through structured data entry, document uploads and Clinical Voice
Scribe.
• More structured MDT preparation by organizing clinical information, AI-assisted findings, consensus
outcomes and reports.
• Clinician-centered AI with review and validation kept within the clinical workflow.
• Improved collaboration across clinical perspectives through a common digital environment.
• Hospital-specific flexibility for workflows, report structures, roles and MDT templates.
• Centralized case information covering clinical records, documents, treatment history and disease
progression.
• Support for different user groups including hospitals, doctors and public users.
• A scalable architecture designed to evolve with healthcare organizations, AI capabilities and future
integrations.
Subscription Model
The case study describes flexible subscription options for public users, individual clinicians and
hospitals/healthcare organizations. Plans can be tailored around user access, case volume,
collaboration, reporting, analytics, integrations and organizational requirements.
Doctor Pro — Individual Clinicians & Public Users
Designed for individual doctors, healthcare professionals and public users, with limited access to
cases.
Hospitals and Clinics
Designed for clinics and hospitals managing clinical teams, with limited user and case access.
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6. Technologies, Governance & Delivery
Context
The case study identifies a set of tools used across collaboration, AI analysis, architecture,
source-code management and project management.
Tool / Technology Role described on the case study
Microsoft Teams Team communication and collaboration
Claude AI-powered clinical analysis and summarization
Lucidchart Visualizing diagrams and architecture
Git Source code management
Jira Project management
Meta Llama AI-powered clinical decision support
Compliance & Standards
The case study states that DiagnomIQ is designed with privacy, security, clinical governance, data
protection, cybersecurity, interoperability and responsible AI principles in mind throughout
development, deployment and operation.
Key Takeaway
DiagnomIQ combines structured clinical data capture, multi-agent and multi-model AI, consensus
evaluation and clinician validation into a single clinical decision-support workflow. The approach is
intended to make complex cancer-case assessment more structured, collaborative and efficient
while keeping qualified clinicians at the center of clinical review and decision-making.
Softnotions Technologies
Healthcare AI • Data Integration • Digital Engineering
Website: https://softnotions.com/
Case study: https://softnotions.com/case-studies/diagnomiq/
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