Artificial Intelligence (AI) is rapidly transforming various sectors, and the medical industry is no exception. The integration of AI in healthcare promises to revolutionize patient care, administrative processes, and medical research, offering many benefits and challenges.
AI’s potential in healthcare is vast, with applications ranging from diagnosis and treatment recommendations to patient engagement and administrative activities. Research studies suggest that AI can perform as well or better than humans at key healthcare tasks, such as diagnosing diseases. Algorithms are already outperforming radiologists at spotting malignant tumors and guiding researchers in constructing cohorts for costly clinical trials.
AI technologies of high importance to healthcare include machine learning, natural language processing (NLP), rule-based expert systems, and robotic process automation (RPA). Machine learning, particularly in its more complex forms like deep learning, is increasingly being applied to tasks such as recognizing potentially cancerous lesions in radiology images. NLP, on the other hand, is used for creating, understanding, and classifying clinical documentation and published research. Rule-based expert systems, although slowly being replaced by data and machine learning algorithms, are still widely used for clinical decision support. RPA is used for structured digital tasks for administrative purposes, such as claims processing and medical records management.
AI’s application in diagnosis and treatment has been a focus since the 1970s. More recently, IBM’s Watson has received considerable attention for its focus on precision medicine, particularly cancer diagnosis and treatment. However, implementation issues with AI bedevil many healthcare organizations. While rule-based systems incorporated within Electronic Health Record (EHR) systems are widely used, they lack the precision of more algorithmic systems based on machine learning. This situation is beginning to change, but it is mostly present in research labs and tech firms rather than in clinical practice.
Patient engagement and adherence is another area where AI is making a significant impact. The more patients proactively participate in their own well-being and care, the better the outcomes. AI-based capabilities are being used to drive nuanced interventions along the care continuum, and there is growing emphasis on using machine learning and business rules engines to provoke actions at moments that matter.
AI also has a great many administrative applications in healthcare. The use of AI can provide substantial efficiencies, which are needed in healthcare because, for example, the average US nurse spends 25% of their work time on regulatory and administrative activities. The technology that is most likely to be relevant to this objective is RPA.
Despite the potential benefits, the integration of AI in healthcare also raises several ethical issues around accountability, transparency, permission, and privacy. Many AI algorithms are virtually impossible to interpret or explain, which can lead to issues of transparency and accountability. Mistakes made by AI systems in patient diagnosis and treatment may be difficult to establish accountability for. There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician.
The medical industry is about to experience significant changes due to the influence of AI. It is expected to enhance patient care, simplify administrative processes, and boost medical research. However, it also presents challenges, particularly in terms of ethical considerations and implementation issues. As AI continues to evolve and integrate into healthcare, it is crucial for healthcare institutions, governmental and regulatory bodies to monitor key issues, react responsibly, and establish governance mechanisms to limit negative implications. This powerful technology requires continuous attention and thoughtful policy for many years to come.
References:
- Artificial intelligence in healthcare: Anticipating challenges and opportunities(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640807/)
- The potential for artificial intelligence in healthcare(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/)
- Artificial Intelligence in Healthcare: A Critical Analysis of the Legal and Ethical Implications(https://www.ijsr.net/archive/v8i10/show_abstract.php?id=ART20201860)
- Artificial Intelligence in Healthcare: Review and Prediction Case Studies(https://www.engrj.org/index.php/eer/article/view/44)
- Artificial Intelligence in Healthcare: Past, Present, and Future(https://www.strokejournal.org/article/S1052-3057(17)30479-6/fulltext)
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