Artificial intelligence(AI) is a transformative and disruptive field within computer science, offering the potential to revolutionize both medical practice and healthcare delivery. In this review, we explore the latest advancements in the use of Artificial Intelligence in healthcare, provide a framework for developing efficient, trustworthy, and safe AI systems, and consider the future possibilities for AI-enhanced healthcare solutions.
AI Introduction in to Healthcare
Artificial Intelligence and digital technologies are set to profoundly influence every aspect of the healthcare industry’s value chain, surpassing any prior innovations. We are already observing the implementation of these technologies in healthcare, demonstrating the immense power of AI now and in the future. The next decade will be crucial, as the foundation for AI’s role in healthcare is being established today.
Cloud computing is playing a key role in integrating reliable and secure AI systems into mainstream healthcare. It offers the computational power needed to process vast amounts of data more quickly and cost-effectively than traditional on-premises infrastructure used by healthcare organizations. As a result, many technology providers are actively collaborating with healthcare institutions to foster AI-driven medical innovations and support the ongoing transformation driven by cloud-based technologies.
AI is not one ubiquitous, universal technology, rather, it represents several subfields (such as machine learning and deep learning) that, individually or in combination, add intelligence to applications. Machine learning (ML) refers to the study of algorithms that allow computer programs to automatically improve through experience.ML itself may be categorised as ‘supervised’, ‘unsupervised’ and ‘reinforcement learning’ (RL), and there is ongoing research in various sub-fields including ‘semi-supervised’, ‘self-supervised’ and ‘multi-instance’ ML.
- Supervised learning leverages labelled data (annotated information); for example, using labelled X-ray images of known tumours to detect tumours in new images.
- ‘Unsupervised learning’ attempts to extract information from data without labels; for example, categorising groups of patients with similar symptoms to identify a common cause.
- In RL, computational agents learn by trial and error, or by expert demonstration. The algorithm learns by developing a strategy to maximise rewards. Of note, major breakthroughs in AI in recent years have been based on RL.
- Deep learning (DL) is a class of algorithms that learns by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples. DL has emerged as the predominant method in AI today driving improvements in areas such as image and speech recognition.
How can we develop AI-augmented healthcare systems that are both effective and trustworthy?
To build effective and trusted AI-augmented healthcare systems, the following steps are essential:
- Design and Development: AI solutions should be designed to address the right problems, using a human-centered AI approach. Stakeholder engagement, especially with healthcare users, is crucial for defining the most relevant issues.
- Stakeholder Engagement and Co-Creation: Assemble a multidisciplinary team that includes healthcare professionals, data scientists, researchers, and other experts to collaboratively define goals, success metrics, and milestones.
- Human-Centered AI: Conduct user-centered research to understand the key problems in healthcare, identify appropriate AI solutions, and ensure the AI systems integrate smoothly into existing workflows.
- Experimentation: Pilot new AI tools through stepwise experimentation, gathering feedback from stakeholders to refine the system. This process helps identify the potential users, ethical implications, and effectiveness of the AI system.
- Evaluation and Validation: Evaluate AI tools on statistical validity, clinical utility, and economic utility. Real-time testing and validation in diverse settings are necessary to assess accuracy, clinical relevance, and cost-effectiveness.
- Scale and Diffusion: As AI systems expand, attention must be paid to deployment, model updates, regulatory compliance, and the variation across healthcare systems.
- Monitor and Maintain: Continuous monitoring and maintenance are essential post-deployment. Healthcare organizations, regulators, and AI developers should collaborate to track performance, safety, and any adverse events.
Assess where your organization lies on the maturity curve
Before adopting Artificial Intelligence, organisations must assess their maturity level in analytics and data management. A solid foundation in descriptive analytics, understanding populations, financial risks, and available resources is essential. Without this, AI initiatives may struggle to achieve ROI. Organisational commitment, leadership support, and focus on data integrity and change management are key to success.
Applications of Artificial Intelligence in Healthcare
Artificial Intelligence is transforming healthcare operations across several areas, including patient data management, reimbursement, and precision medicine. By analyzing vast datasets, AI identifies patterns that enhance decision-making, improve treatment accuracy, and reduce administrative costs. It streamlines billing, documentation, and repetitive tasks, allowing healthcare professionals to focus more on patient care. In clinical settings, AI aids diagnosis and treatment planning, offering personalized care, as seen in cancer treatments and genomics. AI-powered tools also support cost-saving initiatives by optimizing hospital stays and reducing readmissions. Overall, AI enhances both clinical outcomes and operational efficiency, improving healthcare delivery.
Summary
Artificial Intelligence is revolutionizing healthcare by optimizing patient data management, enhancing reimbursement processes, and advancing precision medicine. It analyzes large datasets to identify patterns, improving decision-making and treatment accuracy. AI also automates administrative tasks like billing and documentation, allowing healthcare providers to focus on patient care. In clinical settings, Artificial Intelligence supports diagnosis, treatment planning, and personalized care, particularly in oncology and genomics. Additionally, AI helps reduce costs by streamlining hospital operations and minimizing readmissions, ultimately improving both healthcare outcomes and efficiency.
References:
https://onlinedegrees.sandiego.edu/ai-healthcare/
https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
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