Harnessing artificial intelligence for breakthroughs in lung cancer management: are we ready for the future?

Abstract

Artificial Intelligence (AI) is a broad field encompassing the science and engineering of creating intelligent machines and brilliant computer programs. The essence of AI involves the ability of machines to perform tasks that would typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, language understanding, and interaction. Despite numerous attempts, only some universally accepted definitions of intelligence relate to human-like characteristics. The challenge remains to define the computational procedures that constitute intelligent behaviour broadly and consistently. AI can be broadly categorised into several types.Narrow AI (weak AI) is designed for a specific function, such as video games, personal assistants, or recommendation engines. General AI (strong AI) is a sophisticated system that can perform humanlike tasks, such as self-driving cars or hospital operating rooms. Lastly, super AI is a theoretical concept representing a system that surpasses human cognitive abilities in thinking, reasoning, and learning. AI systems typically involve algorithms that enable them to learn from data and adapt to new situations. Critical components of AI include machine learning, deep learning, and natural language processing, which are used in various applications such as computer vision, speech recognition, and expert systems [1]. AI is also not a monolithic field but encompasses multiple perspectives and methodologies. The Human-like AI focuses on simulating human intellect and behaviour on machines; it includes efforts to model common-sense reasoning, expert knowledge in law and medicine, and social behaviour.On the other hand, rational-like AI emphasises the ability of machines to achieve goals based on performance measures; it involves creating systems that act rationally to achieve specified outcomes [2]. AI can amplify human capabilities by analysing vast volumes of data quickly and accurately, freeing up work that requires uniquely human skills like professional judgment, creativity, and customer empathy. A good data strategy is essential for managing legal data, involving recognition, storage, accessibility, and publication, to ensure effective use and management [3].The definition of AI is a term coined by John McCarthy in 1955, which refers to the science and engineering of making intelligent machines [4]. The history of AI spans several decades, with significant milestones and developments that have shaped the field. Between 1943 and 1952, AI transitioned from a concept to tangible experiments and practical applications. From 1952 to 1956, AI surfaced as a unique domain of investigation. This period saw the groundwork laid for the revolutionary technological domain that AI would become. The period from 1956 to 1974 is known as the Golden Age of AI. Researchers and innovators achieved remarkable advancements in the field, driven by their enthusiasm for AI. In 1974, a critical report on AI research led to significant funding cuts and a sense of letdown. This period is known as the First AI Winter. Between 1980 and 1987, AI underwent a renaissance, with renewed interest and advancements in the field. The period from 1987 to 1993 saw another decline in AI research funding and interest, known as the Second AI Winter. Between 1993 and 2011, AI professionals shifted from attempting to match human intelligence to crafting pragmatic, ingenious software tailored to specific tasks [5].AI will profoundly impact various industries, including healthcare, finance, and transportation [6]. AI endeavours to think humanly, engaging in introspection and solving general problems akin to cognitive sciences. It also strives to reason, using logic to represent and reason within domains and to act rationally, where agents perceive and act upon their environments. However, practical issues arise in representing and reasoning within complex domains and acting rationally or designing agents that pe