Decipher the Cipher
Girish Malleshappa Sogi · Contemporary Clinical Dentistry · 2024
In this day and age, humans are interacting with artificial intelligence (AI) more than ever. It is being used at different levels in a wide array of activities in banking, financial markets, education, supply chains, manufacturing, retail, and healthcare and biomedical research. We have probably interacted with machine learning (ML) algorithms and NLP more than we know. Starting from using facial identification unlock features in our smartphones, to autocorrection while typing, social media platforms and e-commerce sites using AI algorithms to predict what we like, navigation and entertainment streaming apps, chatbots, etc., are few instances of how AI has transformed our everyday life. In recent years, AI has been instrumental in changing the way we assimilate information. Although AI is not a new concept, it was first described as ML by Alan Turing in his article “Computing Machinery and Intelligence” in the 1950 Issue of Mind. He postulated that such as humans and machines can also solve problems and make decisions by utilizing available information and inferences. The term “Artificial Intelligence” was first coined at a famous Dartmouth College conference led by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in 1956. However, it had not become a feasible possibility until a decade ago. AI is a generic term for all nonhuman intelligence and applies to computational technologies that emulate mechanisms such as thought, deep learning, adaptation, engagement, and sensory understanding. As already mentioned, AI, also known as machine intelligence, functions such as machines and is based on the fundamental machine hierarchy of input, processing, and output. In dentistry, the input may be voice data (sounds of handpiece), text data (medical or treatment records and experimental parameters), or picture data (spectral or radiographic images and photos) and the output may be disease diagnosis or prognosis and ultimately clinical decision-making. AI has evoked a spectrum of responses from health-care researchers and professionals some consider it a solution to all the existing problems and yet for some, it is too good to be true. In practical terms, AI has the potential to intercept various aspects of patient care, it has been found to be useful in drug discovery, infectious disease surveillance, and even in aiding efficient health-care administration. AI is a transformative force, reshaping how we approach biomedical research. It makes it easier to turn huge quantities of available data into concrete and actionable evidence to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. AI at its core possesses the potential to greatly enhance the quality and efficiency of data handling, analysis, and publication. AI applications can take care of the vast amount of data produced in health research using advanced algorithms, predictive modeling, and ML techniques. Irrespective of all the potential advantages, utilization of AI for health brings afore many ethical, legal, and social concerns, especially issues of accountability, transparency, permission, and privacy, thus regulatory and ethical framework mandated by the Indian Council of Medical Research should be practiced by all relevant stakeholders to minimize the risk of misuse or abuse of this technological advancement. To efficiently leverage AI in healthcare, it is crucial for health-care providers and researchers to understand the pulse of it. In as few words as possible, the future of AI in healthcare will depend on how well we utilize the full potential of these tools for the upliftment of scientific society in an ethical, noble, righteous, and virtuous manner.