Artificial Intelligence (AI), a form of Artificial Intelligence, will be more prominent in the future in all decision-making processes. This includes everything from politics to healthcare. In healthcare, doctors will use AI and machine-learning devices more frequently to improve diagnosis accuracy and identify treatment regimens. The need for healthcare professionals to incorporate AI into their clinical decision-making process, such as acting as mediators between patients and artificial entities. This scenario could lead to a “third-wheel” effect, which could impact the effectiveness of shared decisions in three ways. First, AI recommendations could delay or paralyze clinical findings; second, patients’ symptoms and diagnoses could be misinterpreted when AI classifications are applied to them; third, confusion may arise about the roles and responsibilities of the actors in the healthcare process (e.g., Who is really in charge?). This article outlines these effects and attempts to determine the future impact of AI technology on healthcare.
Introduction
Artificial Intelligence has seen a rise in popularity over the past few years. Some believe that AI will be the future of technology. This is similar to how automation and factory tools shaped the industrial revolutions of computers, and the internet characterized the recent decades. These machine-learning technologies will not be just tools. They will also serve as interlocutors for human operators and can assist in complex tasks that involve reasoning and decision-making. Machine learning is a branch of computer science that focuses on developing algorithms that can learn from experience and the environment. This improves performance over time. Algorithms are capable of detecting patterns, associations, and similarities in data. This allows for predictions about the likely outcome.
Machine learning and AI are used in many common technologies, such as email, social media, and mobile software. AI’s future is not that it will stop working as it does today. Instead, AI can be an active collaborator with humans in many tasks and activities. AIs can analyze large amounts of data in many formats and contents, even when it is constantly changing (Big Data). Artificial intelligence can identify patterns and similarities between data and give human operators outputs that are difficult to achieve by humans, at least not at the same time.
One example of such outputs is medical diagnoses and the identification of treatment regimens to be given to patients. AIs will be increasingly used by health professionals, especially physicians, to obtain more precise, specific, and objective information about their patients. The main area of AI-based innovation in medical practices could be diagnostic decision support. Machine learning devices are trained to classify stimuli using initial examples. One example is that a patient’s TAC can be used to identify tumor types. Another example is the use of images of skin lesions and optical coherence imaging in the case of sight diseases. The same goes for integrating clinical observations with medical tests for other conditions. Although AI implementation in medicine is primarily focused on diagnosis, there are other areas that could be considered, such as optical coherence tomography for sight diseases or the integration of clinical observations and medical tests for other diseases.
The study of AI in healthcare, in its socio-psychological aspects, is still a neglected area. The “Explainable AI Intelligence” field, commonly abbreviated as XAI, is a vital area of research. It focuses on AI’s transparency and ability to explain its elaboration process. The American Agency for Advanced Research Projects for Defense launched a program about XAI, while the European Parliament demands a “right of explanation” in automated decision-making. One problem with AI implementation in professional practice is that it is meant to be used by non-professionals. For example, doctors, marketers, and military personnel are not expected to become experts in AI development or informatics, but they will need to interact with artificial entities in order to make critical decisions in their respective fields. Although one can agree with AI’s outputs and analyses, trusting them is difficult, and responsibilities for decisions that affect “real life” are not easy. Many scholars consider XAI a priority in technological innovation. Miller and his colleagues believe that AI engineers and developers should look to the social sciences to discover what an explanation is and how it can be implemented in AIs capabilities. Vellido suggested that AIs could learn to make their conclusions transparent using visual aids that allow a human user to understand the process. Pravettoni and Triberti argued that XAIs would become fully developed when they were able to communicate with humans in a realistic way (e.g., answering questions, learning basic perspective-taking techniques, etc.). Apart from AI-human interfaces, there is also the topic of AI’s impact on professional practice and the prediction of potential organizational, practical, or social issues that may arise in the context of implementation.
Although AIs are promising for medical practice, the impact of AIs on clinician-patient relationships is still a poorly understood topic. It is possible that the new technologies could have a positive impact on the relationship between patients and clinicians. The introduction of AI in healthcare is changing how patients receive care. AIs will provide information on diagnosis, treatment, and drugs that can be used to help make decisions at any stage of the healthcare journey. This includes making choices about treatment or lifestyle changes, communicating bad news to loved ones, as well as deciding whether to tell relatives about one’s health.
A patient-centered perspective suggests that such care decisions should be made jointly by doctor and patient. This is the reason for the shared decision-making. This idea has been a key part of the discussion on patient-centered care. The number of scientific publications on this topic rose by more than 600% between 2000 and 2013. Studies show that communication between doctors and patients can have a profound effect on patient well-being. It is possible, however, that the concept of “shared decision” could be updated to include artificial entities in the diagnosis and treatment identification.