New Technologies in International Law / Tymofeyeva, Crhák et al.

potential to impact healthcare by producing new and essential insights from the vast amount of digital data created during healthcare delivery to deliver novel solutions. 443 AI refers to machines’ intelligence and includes machine learning, natural language processing, and robotics, with a wide application in healthcare, possessing the potential to contribute to biomedical research, medical education, and healthcare delivery. 444 Some AI experts have proposed that something ‘acts intelligently’ when it does what is appropriate for its circumstances and purposes, is flexible to changing environments and goals, learns from experience, and makes the right choices given its perceptual and computational limitations. 445 Learnability comprises the critical feature of AI, and machine learning (ML), the dominant approach in AI, is responsible for most of the recent technological advancements in the field. Machine learning typically refers to a system that trains a predictive model by identifying data patterns from input and then uses such a model to make useful predictions from new, never-before-seen data. 446 AI algorithms also use supervised and unsupervised machine learning techniques for autonomous decision-making, as these machine learning algorithms automatically learn and improve themselves from experience without being explicitly programmed, resulting in their application to many data types (including images, speech, videos, and text) on complex tasks that involve large amounts of data to produce results that are comparable to and sometimes superior to human experts in terms of both accuracy and efficiency. 447 This ability to analyze large amounts of data and learn independently depicts the potential benefits of AI implementation in promoting the right to health of individuals and increasing access to healthcare by enhancing the proficiency of clinical work, preventing medical errors, and providing data-driven, evidence-based clinical decisions for advancing medical diagnosis, treatment decisions, biomedical research, and service delivery across the full spectrum of healthcare. 448 In healthcare settings, incorporating AI technology can benefit administrative and clinical processes, including patient safety, hospital administration, drug research, and production, and assist healthcare professionals in making expedient and reliable treatment decisions relying exclusively on data. 449 The technological advancements of AI have also improved other aspects of healthcare delivery, especially in the areas of diagnosis and treatment, by enabling real-time patient information to be easily accessible for physicians, paving the way for fast care management in specific scenarios, 443 Sousa WG et al, ‘How and Where Is Artificial Intelligence in the Public Sector Going? A Literature Review and Research Agenda’ (2019) 36 Government Information Quarterly 101392. 444 Ramesh A et al, ‘Artificial Intelligence in Medicine’ (2004) 86 Annals of The Royal College of Surgeons of England 334. 445 Poole DL, Mackworth AK, Artificial Intelligence: Foundations of Computational Agents (CUP, 2010). 446 Ali S et al, ‘Explainable Artificial Intelligence (XAI): What We Know and What Is Left to Attain Trustworthy Artificial Intelligence’ (2023) 99 Information Fusion 101805. 447 Kalmady SV et al, ‘Towards Artificial Intelligence in Mental Health by Improving Schizophrenia Prediction with Multiple Brain Parcellation Ensemble-Learning’ (2019) 5(1) Schizophrenia 2. 448 Osop H, Sahama T, ‘Data-Driven and Practice-Based Evidence: Design and Development of Efficient and Effective Clinical Decision Support System’ in Moon JD, Improving Health Management through Clinical Decision Support Systems (IGI Global, 2016). 449 Madsen LB, Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry (Wiley, 2014).

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