Event Schedule

The ongoing technology - centric digital transformation is increasing data complexity and neglecting the technical debt it creates. How can you cope when multiple systems, applications, and platforms transform data in an opaque manner and exchange it in non - standard ways?

  • Consider the specific and complex features that healthcare data renders
  • Implement a data - centric approach to counterbalance fragmentation and complexification
  • Decouple technology from data to recover freedom - to - operate with IP - relevant assets (data, information, knowledge) and aim at data sovereignty
  • Leverage AI/ML and semantic web principles, with data governance 4.0

The demand for effective therapies, particularly personalized therapies, has never been more apparent. Unfortunately, the majority of novel cancer therapeutics consist of failed drug trials, with most cohorts refractory to current treatment.FDA states that the oncology - related field that counts for the largest number of AI devices is cancer radiology at 54.9% followed by pathology at 19.7%.

  • Process algorithms to support vector machine (SVM) and random forest (RF) for ligand or structure - based virtual screening
  • Discover methodologies of AI/ML biomarker prediction and high - throughput screening of cancer responses
  • Identify appropriate patient populations in clinical trials
  • Use experimental and virtual methods to screen extensive compound libraries to determine the efficacy

Discuss essential factors of AI and ML and their impact on precision medicine, biomarkers, target identification and screening. Vantage Market Research’s recent analysis of the Global Artificial Intelligence in Healthcare Market is said to reach USD 95.65 Billion by 2028, up from USD 6.60 Billion in 2021.

  • Prepare for the data fuelled machine learning opportunities of the future
  • Revamp drug discovery through the power of microscopy images
  • Using AI to digitally transform the drug discovery industry

When properly implemented, NLP can help with text classification, recognition of syntax, interpretation of word meaning based on location in a sentence, and language translation. Combined with machine learning, it can reinterpret and correct initial assumptions following repeated usage.

  • Extend search and export capability for clinicaltrials.gov
  • Combine drug labels and patent claim extraction to interpret legal strategies
  • Discover the potential and future possibilities of NLP
  • Review a variety of predictive statistical and mathematical modeling techniques

  • Use artificial intelligence (AI) and machine learning (ML) to capture, analyze and aggregate, the landscape of real-world data (RWD) and real-world evidence (RWE)
  • Understand the evolving nature and potential to support the increased and end-to end use of RWE to improve global health and healthcare
  • Discuss several key challenges and barriers, such as siloed databases, interoperability of data capture system, protected health information, and patient diversity

One of the greatest challenges in design is dealing with the subjectivity and variability introduced by human raters when measuring endpoints. We hypothesized that robotic measures, when suitably scaled and combined, can be used to derive robotic biomarkers that can significantly reduce the sample size required to power future studies in stroke, spinal cord injury, and other diseases involving loss of motor function.

  • Understand the underlying computational methodologies that are broadly applicable and easily extend to other many other areas of digital medicine
  • Learn about a comprehensive case study of a robotic assay to measure arm movement
  • Use a combination of artificial ant colonies and neural network ensembles

Public health and drug development is are synonymous with each other. Grasp a better understanding of the interactions with the human environments and how AI plays a major role.

  • Be sure to include the negative part of the process and discuss pitfalls in drug development with the FDA and European approval process
  • Play into the future ability to perform R&D including Medicaid and NHS
  • Review long term revenues and outcomes in drug development

Day 1 Concludes

Precision medicine is an emerging model for the next generation of clinical care that will capitalize relations between biology, lifestyle, behavior and environment. Precision medicine along with AI and ML can discover new drug targets, repurpose the current existing ones or eventually guide the decision-making protocol.

  • Determine how AI, machine learning, and deep learning and big data analytics are evolving to be a great aid to precision medicine.
  • Verify AI/ ML algorithms can learn from heterogeneous datasets
  • Apply genome sequencing powered by AI/ML to a large population

While AI might sound like a promise for the future we should not fool ourselves: it is happening now and it will impact all of us. Our companies, our processes, and not unimportantly ourselves. And it’s just the beginning.

  • Discover how the Metaverse, GPT-3, and DeepFakes will create new efficiencies and opportunities
  • Understand why you, at the personal level, need to evolve to get the most out of new technologies
  • Take a high-level look back at the early days of AI explaining why only now AI becomes so pervasive in our lives

Discuss software solutions to apply machine learning to drug discovery and computational toxicology. Describe the development, benchmarking, and testing of this suite of tools and propose how they might be integrated into the drug discovery pipeline to help increase the efficiency of the design - make - test cycle.

  • Discover ML models discovering novel compounds and optimize properties
  • Review a hill - climb algorithm which makes use of SMILES - based recurrent neural network (RNN) generative models
  • Understand analog generation software and retrosynthetic analysis to score molecules for their synthetic feasibility

Putting resources into advancements can drive new and effective business skills, leading you towards a marketplace upper hand. Drive utilization to promote top - down development while bringing ground - breaking drugs to patients faster. Findings from a McKinsey 2021 survey indicate that AI adoption is continuing its steady rise: 56 percent of all respondents report AI adoption in at least one function, up from 50 percent in 2020.

  • Speed up digital transformation in the business model
  • Hear challenges and pitfalls of implementing AI
  • Discover latest use cases in AI in key research areas

Hear how Aria’s AI-platform can save years from project initiation to in vivo results while generating a 30x hit rate at those milestones, compared to traditional methods. Mitigates clinical drug development risks with traceable and rationalized predictions, to help its researchers evaluate both Phase 1 safety and Phase 2 efficacy for potential drug candidates.

  • Understand how to identify hits for 1000+ diseases
  • Select hits with the best chance of preclinical and clinical success
  • Gain insight on the latest results for disease programs in systemic lupus erythematosus (SLE), idiopathic pulmonary fibrosis (IPF), chronic kidney disease (CKD), and nonalcoholic steatohepatitis (NASH)

Conference Concludes