AI-powered precision care is here – what you need to know

Dr. Anmol Kapoor, founder of CardiAI and BioAro, is an expert in precision healthcare, artificial intelligence and blockchain technologies. He has filed more than seventy patents in areas ranging from AI and genomics to quantum sciences and digital health. His work aims to transform modern healthcare through personalized, precision-based approaches.

We spoke with Kapoor recently to tap into his expertise and gain a deeper understanding of these emerging areas of healthcare innovation. We discussed AI-driven genomic analytics, explored where there are strategic opportunities for top management in precision medicine, and discussed how distributed ledger technology can improve data security and trust in healthcare systems pursuing genomics.

He also discussed how multi-omic integration can provide a competitive advantage for these healthcare systems – and described how clinical leaders and top management must navigate ethical and regulatory challenges as AI-powered precision medicine becomes more common.

Q: How does artificial intelligence help with genomic analysis, and how can it provide hospitals and healthcare systems with a strategic opportunity to improve patient outcomes and operational efficiencies?

A. AI-driven genomics and precision medicine offer hospitals and healthcare systems a significant opportunity to improve their services and improve employee well-being. By leveraging genomic and microbiome data, C-suite executives can implement targeted workplace wellness programs.

These initiatives may include screening and monitoring employees to identify predispositions to cardiovascular, metabolic or mental health disorders. Personalized health plans that include lifestyle and nutritional changes can help reduce lost man-hours and promote overall well-being. In addition, mental health support can be strengthened by identifying at-risk individuals and providing access to counseling and stress management programs.

The microbiome, which includes the microorganisms in our bodies, plays a crucial role in both physical and mental health. The gut-brain axis illustrates how gut health affects mental well-being.

By monitoring patients and employees, healthcare systems can develop dietary interventions to reduce stress, anxiety and depression while increasing productivity. These interventions can lead to better energy levels and better health of employees.

AI-based guidance is especially useful in high-stress industries such as airlines and mining, where attention to detail and long working hours are common. AI tools can analyze employee data to optimize job suitability, determine optimal working hours, and suggest appropriate break times and job changes.

This approach minimizes burnout and increases overall productivity. Machine learning-powered predictive analytics can develop models to assess how work environments, stress levels and genetics influence health and productivity.

Additional, Wearable health technology integrated with AI can continuously monitor health data such as heart rate and stress levels in real-time, providing valuable insights to fine-tune workplace conditions and ensure employee well-being.

Q. Why do you believe blockchain is an ideal technology to secure sensitive genomic information?

A. Blockchain technology provides an irreversible and non-editable robust framework, making it highly effective for protecting genomic and multi-omics data. Once the data is captured, it cannot be changed or deleted, preventing unauthorized changes. Using encryption and decentralized identifiers, genomic data remains confidential even if accessed without permission.

Additional, blockchain can efficiently process large amounts of genomic data, keeping the network fast and reliable as data volumes grow.

Smart contracts can give individuals ownership and control over their genomic data, which they can assign or revoke in real time. This creates a transparent audit trail for every data access, increasing accountability.

The secure exchange of health records between healthcare providers and researchers using blockchain not only supports a more connected healthcare ecosystem, but also protects individual privacy. It can also be helpful to maintain ongoing regulatory compliance.

Q. You suggest that integrating genomics, proteomics and metabolomics using AI can create a competitive advantage for healthcare systems. Please explain this further.

A. AI-based integration of multi-omics data enables personalized medicine, enabling precise and personalized management of patients with minimal adverse effects.

AI algorithms can help identify biomarkers, which can be used for early disease detection and risk prediction through the use of predictive analytics, making healthcare proactive and less reactive.

It also helps accelerate research and development to identify novel biological disease pathways and mechanisms that lead to innovative therapies and improved clinical decision support for healthcare providers.

Q. As hospitals and healthcare systems adopt AI-driven genomic technologies, executives at the highest levels must address ethical, regulatory and legal challenges. How should leadership ensure ethical data use, patient consent, and compliance with evolving regulations?

A. Ethical challenges facing hospitals and healthcare providers primarily stem from patient privacy and consent, with high potential for abuse and breach. Biases in AI/ML models and the complex design of AI algorithms can raise concerns about transparency and accountability.

C-suite executives, both inside and outside a healthcare organization, must create a culture of transparency and ethical data use, where the interests of patients and employees are central. This can be achieved by establishing ethics review boards and involving members of the general public in policy decision-making processes.

Technological advances often outpace regulations, making it difficult to address the regulatory challenges surrounding the use of AI-driven genomic data. Proactive compliance with HIPAA, PIPEDA, and GDPR, and regular audits and reviews of data handling practices, are needed.

Clear guidelines for AI applications in healthcare are also critical, and training staff on compliance and data protection standards can enhance the organization’s ability to effectively navigate the regulatory landscape.

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