Artificial Intelligence in Healthcare Analysis: PMWC 2023 Silicon Valley

We are in an era when the amount of healthcare data is growing at lightning speed, but our ability to gain insights from that data to improve health outcomes is still far behind. The barriers are many and include information silos, poor data quality from unstructured electronic health records, untapped genomics, and persistent health care inequities. Bringing together stakeholders from patients to biotechnology, health systems, pharma, big data and more will accelerate data-driven healthcare. William Oh, MD, Clinical Professor, Mount Sinai and Track Chair AI and Data Science in Drug Discovery & Drug Discovery & Clinical Research, PMWC 2023 Silicon Valley, January 25-27

Artificial intelligence is seen as having the potential to be the most disruptive technological innovation in our lives. AI applications are being embraced and leveraging different data types and structures (structured, unstructured and semi-structured) for integrated healthcare operations across the healthcare sector. Healthcare data provides organizations with a roadmap for improving patient outcomes, optimizing operations (i.e. analyzing workflow, financial, and resource needs), creating new healthcare models (eg, preventive care programs), improving clinical trials, and – when drug discovery and side effects. Development – Accelerate and improve therapeutic candidate selection.

Despite understanding the importance of AI and data science, key challenges must be addressed in order to successfully harness the full potential of AI processes and applications, whether that be selecting and implementing the appropriate computing infrastructure, integrating different types of data, achieving and maintaining data quality, and unifying the teams that It works with different tools and languages, breaking down data silos, addressing and enforcing trust in data, security and governance issues.

Track 2/Day 3 focuses on addressing exactly these challenges during PMWC 2023 Silicon Valley, January 25-27. We have high-level experts across the medical and pharmaceutical sectors contributing to this important track with the aim of bringing key stakeholders together so that knowledge can be shared and challenges and needs can be discussed, and we hope that working towards a consensus approach can be achieved to help move this field forward to accelerate discovery of therapeutics and clinical research. and patient outcomes. The focus will be on making the data more valuable. Key topics in this context are FAIR data principles, NLP applications for unstructured text analysis, AI applications for clinical trial design and patient selection, AI applications for outcome prediction and decision support, and the pharmacokinetic data information ecosystem.

Developed path:
• PMWC 2023 Luminary Award Honors Judd Getz (Broad Institute) for pioneering widely used tools for cancer genome analysis.
• How Healthcare Can Solve Its Data Problem – Speaking by Rod Tarrago (AWS)
• Omics data analysis using new AI strategies provides insights and applications in healthcare – Speaking by Michael Snyder (Stanford University)
• NLP Applications to Analyze Unstructured Medical Text – Session chaired by William Oh (Mount Sinai) with Rong Chen (Sima4)
• AI/Machine Learning applications in the hospital/clinical setting, for clinical trial design and patient selection – with Matthew Longren (Nuance/Microsoft)
• Improving trial probability and organizational success – panel chaired by Elizabeth Lamont (Medidata AI)
• AI/Machine Learning Applications for Patient Outcome Prediction and Clinical Decision Support – Panel chaired by Alex Sherman (Harvard University) with Thomas Fox (Mount Sinai), Endo Navarre (EverythingALS), Nurai Yurt (Novartis Oncology), Jake Donoghue (Bacon), and Mary Abel (Harvard University)
• A FAIR DATA APPROACH TO MAKING DATA USABLE, ACCESSIBLE, AND FINDABLE – Bhavish Patel Talk (Calmi2)
• Pharma Information Ecosystem – Committee chaired by Maria Karasarides (BMS) with Colleen Hill (GNS) and Christine Pacan (Roche/Genentech)

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