AI in Healthcare & Pharma Summit schedule

• How can generative AI techniques be applied to enhance diagnosis and treatment in healthcare and pharma?
• What are the latest advancements in generative AI algorithms for healthcare and pharmaceutical applications?
• How can generative AI models generate synthetic medical data for training and research purposes?
• How can generative AI improve personalized medicine by tailoring treatments to individual patients?
• What are the potential future directions and possibilities for generative AI in the healthcare and pharmaceutical sectors?

• What are the legal requirements and regulations governing the use of AI in healthcare and pharma beyond FDA approval?
• How does AI impact patient privacy, data protection, and security in the healthcare and pharmaceutical domains?
• What are the potential liabilities and risks associated with using AI in healthcare and pharma, and how can they be mitigated?
• How can AI be effectively integrated into existing legal frameworks and healthcare systems?
• What ethical considerations should be taken into account when developing and deploying AI technologies in healthcare and pharma?

Kausheek is a global leader with 15+ years experience in Healthcare & IT– steering and leading teams, products & programs. He has diverse leadership technology experience across multiple domains in Healthcare - data science, marketing & sales , legal & compliance and overall now current role as Head of IT in Boehringer Ingelheim Canada. His passion and focus is building , driving adoption of digital products with co creation and external partnerships; and bringing more health to patients with AI and other technology interventions. His mantra is learn, unlearn and relearn and has a positive and open attitude to collaborate.

• How can generative AI techniques be leveraged to enhance medical imaging for improved diagnosis and treatment?
• What are the recent advancements in generative AI models specifically designed for medical imaging analysis?
• How can generative AI assist in generating high-quality and realistic medical images for training and research purposes?
• What are the challenges and limitations of using generative AI in medical imaging, such as interpretability and generalizability?
• How can generative AI models aid in the development of computer-aided diagnosis (CAD) systems for more accurate and efficient medical image interpretation?

• How can semi-supervised learning techniques be utilized for image classification in radiology?
• What are the advantages and limitations of semi-supervised learning compared to traditional supervised learning in radiology image classification?
• How can unlabeled data be effectively leveraged in combination with limited labeled data to improve the performance of image classification models?
• What are the common semi-supervised learning algorithms and methodologies employed in radiology image classification?

• How can high-throughput screening (HTS) techniques be accelerated and optimized using AI in the context of drug discovery?
• What are the challenges and limitations of traditional HTS approaches, and how can AI help overcome them?
• How can AI algorithms and machine learning models be applied to analyze and interpret large-scale HTS data for identifying potential drug candidates?
• What are the different strategies and methodologies for integrating AI with HTS workflows to improve the efficiency and effectiveness of drug discovery?
• How can AI-driven virtual screening techniques be utilized to prioritize and identify promising compounds for further experimental validation?

• How can quantum generative models be utilized to design novel molecules in drug discovery and materials science?
• What are the advantages of using quantum generative models compared to classical generative models in molecule design?
• How do quantum generative models leverage quantum physics principles to generate and optimize molecular structures?
• What are the challenges and limitations of using quantum generative models in designing molecules, such as scalability and computational resources?
• How can quantum generative models assist in exploring chemical space and identifying molecules with desired properties or target activities?

• How can machine learning and network analysis be utilized for drug repurposing, which involves identifying new therapeutic uses for existing drugs?
• What are the advantages of using machine learning and network analysis compared to traditional approaches in drug repurposing?
• How can machine learning models be trained to predict the potential effectiveness of existing drugs against different diseases or conditions?
• How can machine learning and network analysis contribute to understanding the mechanisms of action and off-target effects of repurposed drugs?

• What are the ethical considerations surrounding the use of AI for clinical trial recruitment and retention?
• How can AI algorithms and machine learning models be ethically employed to identify potential participants for clinical trials?
• What are the challenges and limitations in using AI for clinical trial recruitment, such as biased selection, privacy concerns, and informed consent?
• How can AI tools ensure fairness and inclusivity in participant selection, addressing potential biases related to demographics, socioeconomic factors, or health disparities?
• What are the privacy and data protection measures that need to be in place when using AI for clinical trial recruitment and retention?

• How can AI-driven health advances be achieved while preserving and protecting patient privacy?
• What are the privacy concerns and risks associated with using AI, and how can they be addressed?
• What are the regulatory frameworks and guidelines in place to ensure patient privacy in AI-driven healthcare applications?
• How can privacy-preserving machine learning techniques, such as federated learning, differential privacy, and homomorphic encryption, be utilized to enable AI-driven health advances?
• What are the strategies for securely collecting, storing, and sharing sensitive patient data in AI applications without compromising privacy?

• How can NLP algorithms process and analyze large volumes of clinical text data, such as medical records or nursing notes, to extract relevant information for patient care?
• How can chatbots be developed to provide personalized and interactive support to patients, addressing their healthcare questions, concerns, and needs?
• What are the challenges and considerations in implementing NLP and chatbots in nursing care, including data privacy, patient trust, and integration with existing healthcare systems?
• How can NLP assist in clinical decision-making by providing real-time information, evidence-based guidelines, and predictive analytics to nurses?

• What are the challenges and limitations of traditional manual approaches in case processing, and how can AI address these issues?
• How can AI algorithms be trained to automatically extract relevant information from diverse data sources, such as adverse event reports, electronic health records, and social media data?
• What are the potential applications of natural language processing (NLP) and machine learning in automating case processing tasks, such as case triage, data extraction, causality assessment, and signal detection?
• How can AI assist in automating data reconciliation, quality control, and error detection in pharmacovigilance processes?

What are the benefits of using AI and IoT in remote patient monitoring compared to traditional methods?
How can wearable devices equipped with sensors and AI algorithms collect and analyze real-time patient data, such as vital signs, activity levels, sleep patterns, or medication adherence?
What are the challenges and considerations in implementing AI-powered remote patient monitoring, including data security, data integration, and user acceptance?
How can AI algorithms detect and alert healthcare providers to anomalies or critical events in patient data, enabling timely interventions and proactive care?
How can remote patient monitoring using AI and IoT improve patient outcomes by enabling early detection of deteriorating health conditions and reducing hospital readmissions?

• How can machine learning-based Clinical Decision Support Systems (CDSS) be utilized to predict patient outcomes?
• What are the advantages of using machine learning in predicting patient outcomes compared to traditional methods?
• What types of data sources, such as electronic health records, imaging data, or genomic data, can be integrated into machine learning models for outcome prediction?
• How can machine learning algorithms be trained to analyze and interpret complex patient data to predict clinical outcomes, such as disease progression, treatment response, or adverse events?
• What challenges and considerations are in developing machine learning models for predicting patient outcomes, including data quality, feature selection, and model explainability?

• How can deep learning and natural language processing (NLP) be used to uncover patient subgroups for clinical trials?
• What are the advantages of using deep learning and NLP compared to traditional approaches in identifying patient subgroups?
• How can deep learning models be applied to analyze and extract meaningful information from unstructured clinical text data, such as medical records, physician notes, or patient surveys?
• What are the challenges and considerations in applying deep learning and NLP techniques to uncover patient subgroups, including data preprocessing, model interpretability, and generalizability?
• What are the strategies for integrating structured and unstructured data sources to capture a comprehensive view of patient characteristics and identify relevant subgroups for clinical trials?

• What are the advantages of using machine learning in clinical trial design compared to traditional manual approaches?
• How can machine learning algorithms analyze and interpret various data sources, such as patient data, trial outcomes, and historical trial data, to inform trial design and protocol optimization?
• What are the challenges and considerations in developing machine learning models for automated clinical trial design, including data quality, bias, and generalizability?
• How can machine learning assist in the identification of appropriate patient populations, treatment arms, and endpoints for clinical trials?

• How can machine learning be applied to unravel complex medical conditions and improve diagnosis?
• What are the advantages of using AI-enabled diagnosis compared to traditional diagnostic approaches in complex medical conditions?
• How can machine learning models be trained to analyze and interpret complex medical data, such as imaging, genomics, or electronic health records?
• What are the challenges and considerations in developing machine learning models for complex medical conditions, such as data quality, interpretability, and model validation?
• How can AI-enabled diagnosis assist in early detection, risk stratification, and personalized treatment planning for complex medical conditions?

• How can machine learning be used to predict drug response and optimize personalized treatment plans?
• What are the advantages of using machine learning in predicting drug response compared to traditional approaches?
• What types of data sources, such as genomic data, clinical data, or biomarker data, are utilized in machine learning models for predicting drug response?
• How can machine learning algorithms be trained to analyze and interpret complex data to predict individual patient responses to specific drugs or treatments?

• How can AI contribute to enabling earlier medical diagnosis?
• What are the benefits of using AI in improving the accuracy of medical diagnosis compared to traditional approaches?
• How can AI algorithms analyze and interpret large volumes of patient data, such as medical images, lab results, genetic information, or patient records, to aid in early detection of diseases?
• How can AI assist healthcare professionals in identifying subtle patterns or biomarkers that may not be easily detectable by human analysis alone?
• How can AI support the development of decision support systems that provide clinicians with real-time insights and recommendations during the diagnostic process?