• Artificial intelligence technology
• Artificial intelligence in healthcare
• Artificial intelligence definition

Artificial intelligence general

Generative AI saw a rapid growth in popularity following the introduction of widely available text and image generators in 2022, such as ChatGPT, Dall-E and Midjourney, and is increasingly applied in business settings https://timebusinessnews.com/unlocking-the-full-potential-of-aisdr-ai-based-solutions-for-scalable-sales-growth/. While many generative AI tools’ capabilities are impressive, they also raise concerns around issues such as copyright, fair use and security that remain a matter of open debate in the tech sector.

The two presented their groundbreaking Logic Theorist, a computer program capable of proving certain mathematical theorems and often referred to as the first AI program. A year later, in 1957, Newell and Simon created the General Problem Solver algorithm that, despite failing to solve more complex problems, laid the foundations for developing more sophisticated cognitive architectures.

Explainability, or the ability to understand how an AI system makes decisions, is a growing area of interest in AI research. Lack of explainability presents a potential stumbling block to using AI in industries with strict regulatory compliance requirements. For example, fair lending laws require U.S. financial institutions to explain their credit-issuing decisions to loan and credit card applicants. When AI programs make such decisions, however, the subtle correlations among thousands of variables can create a black-box problem, where the system’s decision-making process is opaque.

The late 19th and early 20th centuries brought forth foundational work that would give rise to the modern computer. In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada King, Countess of Lovelace, invented the first design for a programmable machine, known as the Analytical Engine. Babbage outlined the design for the first mechanical computer, while Lovelace — often considered the first computer programmer — foresaw the machine’s capability to go beyond simple calculations to perform any operation that could be described algorithmically.

Artificial intelligence technology

Some researchers are discussing the idea of building organisms from molecules with reversed structures. An interdisciplinary group says potential consequences include untreatable infections and irreversible ecosystem disruption.

No, artificial intelligence and machine learning are not the same, but they are closely related. Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. Machine learning helps a computer to achieve artificial intelligence.

Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.

Computer vision is another prevalent application of machine learning techniques, where machines process raw images, videos and visual media, and extract useful insights from them. Deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for image recognition, image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars and robots.

Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. In 1967 Marvin Minsky agreed, writing that “within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved”. They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky’s and Papert’s book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The “AI winter”, a period when obtaining funding for AI projects was difficult, followed.

Artificial intelligence in healthcare

We report that the public is concerned about the breach of patient privacy and data security by AI tools. As reported by 5(7%) of the articles reviewed, AI tools have the potential to gather large volumes of patient data in a split of a second, sometimes at the blind side of the patients or their legal agents. As argued by Morgenstern et al. and Richardson et al. , given their sheer complexity and automated abilities, it will be difficult to foretell when and how a specific patient data are acquired and used by AI tools, a tuition the presents a ‘black box’ for patients. Thus, apart from what the patient may be aware of, there was no surety of what else these machine clinicians could procure, albeit unlawfully, about the patient. Furthermore, it is unclear how patient data are indemnified against wrongful use and manipulation . These AI tools could, wittingly or unwittingly disclose privileged information about a patient with potentially dire consequences for the privacy and security of patients. It is expected that patients would be apprehensive about the privacy and security of their personal information stored by AI tools . Given that these AI tools could act independently, patients would naturally be worried about what happens to their personal information.

Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, Moreno Franco P, Diedrich DA, Pickering BW, Herasevich V. Applied artificial intelligence in healthcare: a review of computer vision technology application in hospital settings. J Imaging. 2024.

Artificial intelligence is not one technology, but rather a collection of them. Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. Some particular AI technologies of high importance to healthcare are defined and described below.

Considering the social implications, this review is envisaged to positively impact the development, deployment, and utilisation of AI tools in patient care services . This is anticipated as the review to interrogate the main concerns of the patients and the general public regarding the use of these intelligent machines. The preposition is that these tools have the possibility for unpredictable errors, couple with inadequate policy and regulatory regime, may increase healthcare cost and create disparities in insurance coverage, breach privacy and data security of patients, and provide bias and discriminatory services which can be worrying . Therefore, the review envisaged that manufacturers of AI tools will pay attention and factor these concerns into the production of more responsible and patient-friendly AI tools and software. Additionally, medical facilities would subject newly procured IA tools and software to a more rigorous machine learning regime that would allay the concerns of patients and guarantee their rights and safety . Moreover, the review may trigger the formulation and review of existing policies at the national and medical facility levels, which would provide adequate promotion and protection of the rights and safety of patients from the adverse effects of AI tools .

We report that the public is concerned about the breach of patient privacy and data security by AI tools. As reported by 5(7%) of the articles reviewed, AI tools have the potential to gather large volumes of patient data in a split of a second, sometimes at the blind side of the patients or their legal agents. As argued by Morgenstern et al. and Richardson et al. , given their sheer complexity and automated abilities, it will be difficult to foretell when and how a specific patient data are acquired and used by AI tools, a tuition the presents a ‘black box’ for patients. Thus, apart from what the patient may be aware of, there was no surety of what else these machine clinicians could procure, albeit unlawfully, about the patient. Furthermore, it is unclear how patient data are indemnified against wrongful use and manipulation . These AI tools could, wittingly or unwittingly disclose privileged information about a patient with potentially dire consequences for the privacy and security of patients. It is expected that patients would be apprehensive about the privacy and security of their personal information stored by AI tools . Given that these AI tools could act independently, patients would naturally be worried about what happens to their personal information.

Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, Moreno Franco P, Diedrich DA, Pickering BW, Herasevich V. Applied artificial intelligence in healthcare: a review of computer vision technology application in hospital settings. J Imaging. 2024.

Artificial intelligence definition

Playground AI refers to experimental or beginner-friendly environments where users can explore and interact with AI technologies. It often involves tools or platforms designed for educational purposes or creativity with AI.

The term artificial intelligence is closely linked to popular culture, which could create unrealistic expectations among the general public about AI’s impact on work and daily life. A proposed alternative term, augmented intelligence, distinguishes machine systems that support humans from the fully autonomous systems found in science fiction — think HAL 9000 from 2001: A Space Odyssey or Skynet from the Terminator movies.

Over the decades, the field of AI has evolved significantly, with the development of various techniques and technologies, such as machine learning, deep learning, and natural language processing. The 1980s and 1990s saw a surge in the popularity of expert systems, which were designed to mimic the decision-making process of human experts. In the 2000s, the rise of big data and powerful computing resources paved the way for the development of more advanced AI systems, leading to breakthroughs in areas like computer vision, speech recognition, and autonomous vehicles.

In summary, these tech giants have harnessed the power of AI to develop innovative applications that cater to different aspects of our lives. AI is at the heart of their offerings, from voice assistants and virtual agents to data analysis and personalized recommendations. Through the intelligent integration of AI technologies, these companies have shaped the landscape of modern technology and continue to push the boundaries of what is possible.

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