Driving the Future of Advanced Clinical Development

Advancing Evidence-Based Medicine Through Causal Inference & Explainable AI.

Our Purpose

Our expertise lies in developing multi modal artificial intelligence frameworks that advance precision medicine by combining causal inference with explainable deep learning methods to support evidence-based clinical decision making.

Our Comprehensive Services

Comprehensive overview of our specialized services in causal inference and explainable AI methods

Model performance

Evaluation in precision medicine involving comprehensive assessment across multiple dimensions including predictive accuracy, clinical utility, and real-world effectiveness

  • Positive and negative predictive values for clinical action ability
  • Concordance index (C-index) for survival and time-to-event predictions

Survival Analysis and Kaplan-Meier Curves

We are experts in deploying Machine Learning for Survival analysis to analyze time-to-event data, such as time until death, disease progression, treatment response, or adverse events in clinical studies. The Kaplan-Meier curve is the most commonly used non-parametric approach that estimates the probability of survival over time, accounting for censored observations

Survival curves enable comparison of treatment efficacy between groups, assessment of median survival times, and identification of time-dependent treatment effects. They're essential in oncology trials for overall survival and progression-free survival endpoints, cardiovascular studies for time to major adverse events, and drug safety monitoring for time to adverse reactions

Clinical Decision Support Systems (CDSS)

Integration of Clinician validated Clinical Decision Support Systems in oncology precision medicine

  •  AI-powered treatment recommendation platforms for oncology and precision medicine
  •  Individual treatment effect (ITE) estimation for personalized therapeutic selection
  •  Average Treatment effect (ATE)
  •  Integration of patient demographics, tumor characteristics, biomarkers, and health indicators
  •  Real-time decision support with explainable treatment rationale
  •  Budget optimization and recommendations
Explore All Services

Prefer a short presentation? We can prepare a 5–7 minute slide demo tailored to your clinical team.

Clinical Research Consulting Services

Strategic Clinical Development & Portfolio Advisory

Strategic Clinical Development & Portfolio Advisory services assist biopharma companies in making informed investment decisions by evaluating scientific validity, market potential, and optimizing portfolio strategies through AI-powered insights

  • Help support prepare Clinical Development Plan (CDP) – Phase I - IV
  •  Design out unmet medical need & disease landscape
  •  Indication prioritization & sequencing
  •  Endpoint Strategy (Efficacy, Safety, Surrogates, PROs)
  • Statistical & Adaptive Design Strategy
  •  Safety Management & Pharmacovigilance Strategy
  •  Risk Assessment & Mitigation Strategy
  •  Trial Design & Operational Feasibility Strategy
  •  Site & Country Selection Strategy
  •  Patient Recruitment & Retention Strategy
  •  Diversity & Inclusion Strategy
  •  Medical Affairs & Scientific Communication Plan

Regulatory Affairs & Study Start-Up

Strategic regulatory pathway analysis, health authority engagement, dossier preparation, global submissions, site activation acceleration, and lifecycle change management.

Our Regulatory Affairs and Start-Up services provide comprehensive support throughout the clinical trial lifecycle, from strategic regulatory planning to efficient site activation and post-registration compliance. Leveraging advanced technologies and a global network of experts we offers tailored solutions that streamline regulatory processes, mitigate risks, and accelerate time to market. Our services encompass regulatory strategy development, agency interactions, submission management, and lifecycle maintenance, ensuring that Pharma, biotech and MedTech companies navigate complex regulatory landscapes with confidence and efficiency.

Medical & Scientific Affairs Support

Providing protocol and endpoint consulting, medical monitoring, KOL/SAB engagement, scientific narrative development, publication strategy, and compliant medical communications.

  •  Clinical study document preparation & amendments management
  •  Medical Monitoring & Data Review charters
  •  Endpoint & Outcome Measure Consulting - ClinRO / PRO / ObsRO selection, validation
  • strategy, digital biomarkers
  •  KOL / Scientific Advisory Board (SAB) Planning & Moderation
  •  Publication & Conference Strategy
  •  Publication plan, abstract/manuscript writing, congress slide decks

Technology & Methodologies

A practical deep dive into the technical frameworks and methods we use to build robust, explainable causal AI for healthcare.

Enhanced DragonNet Clinical Report

AI-powered precision oncology treatment recommendations.

AI Enabled Oncology Precision Medicine Clinical Decision Support System

A comprehensive Clinical Decision Support System (CDSS) that integrates artificial intelligence with multimodal oncology data to deliver evidence-based, personalized treatment recommendations at the point of care. The platform pulls information from the genetic, demographics, clinical, treatment and laboratory results through advanced machine learning algorithms to support clinical decision-making.

Causal Inference Framework

  •  Potential Outcomes Framework: Counterfactual reasoning and treatment effect estimation
  •  Directed Acyclic Graphs (DAGs): Causal structure identification and confounder selection

Hybrid causal AI architecture

Propensity Score Methods

  •  Propensity Score Methods: Matching, stratification, and inverse probability weighting
  • Instrumental Variables: Two-stage least squares and weak instrument diagnostics
  • Doubly Robust Methods: Combining outcome modeling with propensity score approaches
  • Mediation Analysis: Direct and indirect effect decomposition

Hybrid causal AI architecture

Hybrid Causal-AI Methods

  • Causal Deep Learning: Neural networks with causal structure constraints
  •  Representation Learning for Confounders: Deep learning approaches to confounder identification
  •  Causal Discovery with AI: Machine learning methods for causal structure learning
  •  Treatment Effect Heterogeneity: AI-powered personalized treatment effect estimation
  • Causal Reinforcement Learning: Decision-making under causal constraints

Hybrid causal AI architecture

About Our Expertise

We specialise in causal inference and explainable AI for clinical and research settings.

Our Mission

Advance evidence-based decision-making by combining rigorous causal inference with transparent, interpretable AI. We translate statistical theory into practical tools that clinicians and researchers can trust.

Expertise Areas

Causal Inference

Treatment effect estimation, confounding control, and causal discovery tailored to healthcare applications.

Explainable AI

Interpretable models and transparent decision systems that support clinician trust.

Statistical Methodology

Novel algorithm development, theoretical foundations and reproducible implementation.

Healthcare Applications

Clinical decision support, evidence synthesis and deployment in care settings.

Research Collaboration

Cross-disciplinary partnerships for translational research and publications.

Research Philosophy

Methodological Rigor

We prioritise statistical validity and theoretical soundness in every project.

Practical Applicability

Methods are developed with real-world constraints and end-user workflows in mind.

Transparency

Open science practices, model cards and reproducible pipelines.

Collaboration

Fostering interdisciplinary teams to translate methods into practice.

Education

Training the next generation of researchers with hands-on workshops and materials.

Interested in Our Research Approach?

Connect with us to explore collaboration opportunities and tailored studies.

Contact Categories

Research Collaboration

  • Joint methodology development projects
  • Cross-disciplinary research partnerships
  • Student supervision and mentoring opportunities
  • Grant application collaborations

Consulting Services

  • Statistical methodology consulting
  • Causal inference study design
  • Explainable AI implementation
  • Method validation and peer review

Our Leadership Team

Meet the professionals driving innovation at the intersection of healthcare and artificial intelligence

Rani Abraham

Rani Abraham

AI-Augmented Precision Medicine & Integrated Evidence

Pharmacologist| AI Scientist/Machine Learning Engineer| Clinical Trials Development Advanced Methods | IEGP| Translational bioinformatics | Adaptive design | Innovative data analytics | Regulatory Affairs| Clinical Ops

Dr. Ibrahim Riza

Dr. Ibrahim Riza

Clinical Translation Officer

UK-trained surgeon with pioneering work in vascular services and hybrid operating rooms. Clinical validator for multi-modal AI models.

Dr. Kavita Raj

Dr. Kavita Raj

Clinical Innovation - Haemato Oncology Precision Medicine

Specializes in leukemia treatment and stem cell transplants. PhD research on Myelodysplastic syndromes with passion for AI in clinical medicine.

Ready to Transform Your Clinical Research?

Partner with evexai to accelerate trials, enhance data quality, and meet regulatory expectations with confidence.