Our Comprehensive AI Augmented 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

Directed Acyclic Graphs (DAGs) - Causal Pathway Mapping

Directed Acyclic Graphs as a foundational method for mapping causal relationships and identifying potential confounders before conducting clinical analyses

Our DAG-based approach systematically visualizes the assumed causal structure between exposures, outcomes, and covariates, enabling us to design statistically sound studies and select appropriate variables for adjustment. This methodology ensures our AI models and statistical analyses account for the correct causal pathways while avoiding common pitfalls like collider bias and over-adjustment

Explainable deep learning methods (XAI) and Causal Inference

XAI and Causal Inference

 Combined with XAI and causal inference techniques, our approach move beyond correlation to establish true cause and effect relationships in clinical data, enabling researchers to understand not just what the AI predicts, but why and whether interventions will actually improve patient outcomes.

Development by Design: Clinical Validation Integration

Clinical collaboration and oversight from conception to deployment.

Our approach to AI model development is fundamentally grounded in clinical collaboration from conception to deployment. Rather than developing models in isolation and seeking clinical input retrospectively, we embed clinical validation as an integral component of our design methodology.

Clinical Co-Development Framework

Collaborative effort between clinicians, Pharma/CRO experts

  • Initial Design Phase: Clinical subject matter experts collaborate in defining research questions, identifying relevant variables, and establishing clinically meaningful outcome measures
  • Architecture Development: Clinicians provide ongoing input on model interpretability requirements, ensuring AI explanations align with clinical reasoning patterns
  • Iterative Validation: Regular clinical review cycles throughout development, with physicians evaluating model outputs against established clinical knowledge and practice guidelines
  • Real-World Testing: Pilot implementations in clinical environments with continuous clinician feedback and model refinement

Continuous Clinical Oversight

Beyond initial validation, our models benefit from ongoing clinical oversight through:

  •  Clinical Advisory Boards: Formal advisory structures with practicing physicians who guide model evolution and clinical application strategies
  •  Outcome Monitoring: Systematic tracking of model performance in clinical settings with physician evaluation of real-world effectiveness
  •  Feedback Integration: Structured processes for incorporating clinician insights into model updates and improvements
  •  Quality Assurance: Regular clinical audits to ensure maintained accuracy and relevance as medical knowledge and practices evolve

This development-by-design approach with embedded clinical validation ensures our AI frameworks are not merely technically sophisticated, but genuinely useful tools that enhance clinical decision-making while maintaining the highest standards of patient safety and care quality.

Multidisciplinary team review

Multidisciplinary teams of specialists evaluate model performance and development of AI models for various therapeutic areas

In addition to Clinicians,  a team of Pharmacologists/Clinical Research Industry experts evaluate the potential algorithm across different patient demographics and clinical presentations. Our "clinician-validated" designation serves as a quality marker to indicate that  AI system's clinical applicability and validation by design. 

Clinician-Validated AI Models

AI models rigorously tested and approved by medical experts to ensure clinical accuracy and patient safety.

Our clinician validated algorithms bridge cutting-edge artificial intelligence with real-world medical expertise, delivering trusted insights that healthcare professionals rely on for critical patient care decisions.

Portfolio Optimization of Pharmaceutical Products Using Genetic Algorithms & Algorithmic Trading

Leveraging advanced genetic algorithms to optimize pharmaceutical and biotech product portfolios, enabling strategic selection of projects that maximize returns, manage risk, and align with long-term R&D objectives.

Our approach provides a dynamic and data driven framework for portfolio decision making in drug development. By incorporating multiple objectives such as maximizing revenue, minimizing costs, ensuring disease area balance, and maintaining phase-wise project distribution our models enable robust, long-term strategic planning. The genetic algorithm evaluates thousands of potential portfolio combinations, factoring in budget constraints, minimum launch requirements, and the inflow of new projects. This allows companies to optimize R&D investments, mitigate risks associated with uncertain clinical outcomes, and maintain a balanced, sustainable pipeline. Advanced extensions can include stochastic modeling for cash flows and risk, multi-objective optimization, and real-time updates as new projects enter the pipeline.

Gene Expression Using PyTorch

We leverage PyTorch to model and analyze high-dimensional gene expression data, uncovering complex patterns, predicting phenotypes, and identifying key biomarkers.

Our approach utilizes deep learning techniques neural networks to process large-scale gene expression datasets. PyTorch’s dynamic computation graph and GPU acceleration enable efficient training on high dimensional, sparse data, capturing nonlinear relationships between genes and phenotypes. This allows for accurate disease prediction, clustering, biomarker discovery, and integration with multi-omics datasets. The framework supports advanced techniques such as transfer learning and dimensionality reduction, providing scalable, robust, and interpretable insights for genomic research and precision medicine.

Predictive analysis for diseases

Capabilities of Machine learning (ML) and artificial intelligence (AI) in Predictive analysis

Techniques in the early detection and diagnosis of disease using logistic regression (supervised analysis) and clustering (unsupervised analysis) in predicting the risk of the disease.