In today’s rapidly evolving technological landscape, artificial intelligence (AI) is playing a pivotal role in reshaping the realm of cloud computing. This dynamic synergy between AI and cloud technology is ushering in a new era often referred to as the “Internet of AI,” where AI capabilities seamlessly merge with cloud computing services. In this Q&A, we will delve into the ways in which AI is revolutionizing cloud computing, the benefits it offers to cloud adopters, potential downsides to consider, and the promising long-term outlook for AI-supported cloud computing.
In what way is AI transforming cloud computing?
AI is acting as a catalyst, compelling cloud providers to enhance their offerings and capabilities. Consequently, we are entering an era where the “Internet of AI” is becoming a reality, with AI and tools surrounding AI itself evolving into a readily available services within cloud computing.
- Shift in Workload Patterns: AI workloads, encompassing both learning and inference processes, are experiencing a significant surge in demand leading to driving computing capacity requirements. This surge is compelling cloud providers to intensify their data and AI offerings in the context of healthcare-related applications. In rapidly evolving AI-driven digital landscape in the healthcare ecosystem, cloud providers are now compelled to provide secure and compliant data encryption services to encrypt patient data, AI model training and deployment capabilities for tasks like medical image analysis and interoperability. Prominent examples of this trend include GCP, Azure, and AWS offering FHIR (Fast Healthcare Interoperability Resources) and medical imaging solutions and healthcare services underscoring their commitment to supporting AI-powered applications.
- Innovative Tools for AI Democratization: Cloud providers are at the forefront of driving innovation in tools such as AI-enhanced diagnostic assistants and health chatbots, Predictive Analytics for Disease management, NLP for clinical documentation etc. These tools, not only facilitate the democratization of AI, but also streamline the development, training, and deployment of AI and machine learning models, making the power of AI accessible to everyone.
- Adoption of Multi-Cloud Strategies: Organizations are increasingly embracing multi-cloud strategies to harness the best capabilities and innovations offered by hyperscale cloud providers to meet the IT and data storage needs of healthcare organizations. Multi-cloud approaches are often used to ensure data accuracy and minimizing data records in MPI (Master Patient Index), data security and efficient data sharing and analysis in clinical trials and research, deliver secure and scalable remote healthcare services, and in healthcare analytics.
How can this trend benefit cloud adopters?
Embracing the trend of AI-as-a-service within the cloud empowers organizations to achieve agility, innovation, and cost-efficiency, ultimately driving their success in the ever-evolving healthcare ecosystem.
- Faster Innovation and Business Transformation: Cloud adopters can innovate at a faster pace and concentrate on their business transformation endeavours instead of diverting resources towards developing complex AI tools or managing extensive compute and storage infrastructures. By harnessing AI-as-a-service offerings within the cloud, they can perform image analysis enhancing patient diagnostics, analyse vast datasets to identify drug candidate in drug discovery phase, deploy virtual chatbots to strengthen patient engagement amongst many other innovative solutions.
- Accelerated AI Adoption: Cloud’s democratization of AI development, through intuitive and guided tools, allows healthcare organizations to accelerate the adoption of AI across various facets of their operations. This means that businesses can more easily integrate AI into their processes, products, and services, enhance patient care and contribute to a more efficient healthcare system by empowering healthcare professionals with valuable insights and tools to deliver higher-quality care.
- Optimized Cost Management: Cloud adopters can optimize their cloud operations’ cost-effectiveness while ensuring maximum availability and business continuity. With AI-driven solutions orchestrating data-driven decision making, scheduling and compliance management, organizations can minimize wastage and inefficiencies, leading to cost savings and improved patient care and lower healthcare costs for patients.
Is there a downside to applying AI to cloud computing?
- Security Risks and Breaches: Integrating AI into cloud computing can introduce new security challenges with respect to sensitive data, specifically PII/PHI, and infrastructure. The more AI is used, the larger the attack surface becomes, potentially leading to an increased risk of data security breaches and data exposures. It’s crucial for healthcare organizations to implement robust security measures, train staff on HIPAA compliance, ensure encryption of patient data and continuously monitor AI systems to mitigate these risks.
- Drift in AI Models: Over time, AI models can experience “drift,” where their performance may degrade due to changes in the clinical data they are trained on or shifts in the environment. This drift can create significant risks as inaccurate diagnoses, incorrect treatment recommendations, incorrect medication dosage and allergy alerts, and legal and ethical issues if AI-driven decisions become less accurate or reliable. Maintaining AI models’ performance and adapting them to evolving conditions is essential to ensure the safety and well-being of patients.
- Change Management Challenges: In healthcare organizations with strict compliance regulations, integrating AI into cloud computing can pose challenges related to change management, such as disrupting existing clinical workflows and integration challenges. Ensuring that AI implementations comply with regulations and standards while effectively managing organizational changes can be complex and requires careful planning and execution.
What’s the long-term outlook for AI-supported cloud computing?
In the long run, AI-supported cloud computing is expected to be characterized by heightened automation, accelerated medical research, improved compliance processes, and improved overall healthcare efficiency. These advancements will not only drive efficiencies but also strengthen the resilience and competitiveness of organizations.
- Automation of Testing and Verification Protocols: AI is poised to play a pivotal role in automating testing and verification protocols in pathology and histology analysis, Diagnostic testing, Pharmaceutical Quality Control, Clinical Trail Data Monitoring, and Medical Device Testing, enhancing treatment planning and improving patient outcomes through quicker and more accurate assessments of medical data.
- Generation of Compliance Records by Gen AI: The emergence of “Gen AI” or the next generation of AI technologies is expected to further revolutionize cloud computing. Gen AI will be capable of autonomously generating and maintaining compliance documents like privacy policies and consent forms as per regulations, medical coding and billing documentation and assisting with clinical documentation like medication records and discharge summaries, reducing human intervention and potential errors. This will streamline compliance efforts and bolster regulatory adherence.
- AI-Driven Observability: AI-driven observability will become increasingly prevalent in cloud computing. AI algorithms will continuously monitor and analyze cloud environments, providing real-time insights into real-time patient data, security, and operational issues. This proactive observability will empower organizations to preemptively alert healthcare providers to critical changes in patient vitals, help prevent equipment breakdowns, reduce patient wait times, minimize congestion, optimize resource allocation, and enhance overall system reliability.
Dhaval Shah
Dhaval Shah is an executive vice president at CitiusTech responsible for developing the vision, capabilities, and solutions for partnering with leading healthcare and life sciences organizations. He has more than two decades of experience in health-care IT, including senior-level roles in engineering, research, software development, IT architecture and management roles serving pharmaceutical companies, physician practices and health insurance companies.
