From the Executive Editor: Unravelling Artificial Intelligence – Essential Insights for Rheumatologists

Keerthi Talari MD, DM (Rheumatology)
Consultant Rheumatologist, Yashoda Hospitals, Secunderabad, Telangana

Artificial Intelligence (AI) has emerged as a transformative force in human lives, revolutionizing the way we approach our day to day personal and professional activities’.  AI refers to the development of computer systems that can perform tasks requiring human intelligence, such as problem-solving, speech recognition, and decision-making. In healthcare, AI is harnessed to enhance the efficiency and accuracy of medical processes. To understand AI it is essential to understand some common terms used in AI. Few of them are-

Machine Learning (ML): ML enables computers to learn from data and improve performance over time.

Algorithm: An algorithm is a set of rules followed by a computer to solve a problem.

Supervised learning – Supervised learning is a machine learning paradigm where the algorithm is trained on a labeled dataset, meaning that the input data is paired with corresponding output labels.

Unsupervised learning – Unsupervised learning is a machine learning approach where the algorithm is provided with unlabelled data and is tasked with finding patterns, relationships, or structures within that data on its own.

Deep Learning: Deep Learning is a subset of ML where algorithms mimic the human brain’s structure (called neural networks) and can take decisions based on complex inputs.

Big Data: Big Data involves processing and analysing massive datasets.

Imagine a restaurant as a bustling kitchen, and Artificial Intelligence (AI) as the master chef orchestrating a seamless dining experience. The chef’s ability to learn from customer feedback represents Machine Learning, refining recipes based on taste preferences. Each dish creation follows an algorithm (recipe), a set of rules guiding the chef’s culinary journey. Supervised Learning is akin to a skilled mentor guiding an apprentice chef. The mentor, representing the algorithm, provides explicit instructions on preparing each dish, offering continuous feedback as the apprentice learns. The process involves a clear mapping of inputs (ingredients) to outputs (dishes), mirroring the labelled training data in AI. On the other hand, Unsupervised Learning resembles a culinary experiment where the chef explores new flavour combinations without specific guidance. The chef identifies patterns and similarities among ingredients independently, creating novel dishes without predefined instructions.  Deep Learning mirrors the chef’s mastery of intricate techniques, while Neural Networks symbolize the collaborative kitchen team working in harmony. Big Data management corresponds to efficiently handling extensive orders and customer preferences for optimized service.

AI in healthcare

AI is transforming health care by aiding in diagnosis, treatment, and research:

Diagnosis: AI analyzes patient data, including symptoms, imaging, and genetic information, providing clinicians/ radiologists with more precise and timely diagnoses.

Treatment: AI assists in personalized treatment plans by considering individual patient factors, improving medication selection, and predicting treatment responses.

Research: AI accelerates research by analyzing vast datasets, identifying patterns, and suggesting novel approaches for understanding different diseases.

While AI offers immense potential, ethical concerns include data privacy, algorithm bias, and the need for human oversight. Rheumatologists must be cautious in integrating AI, ensuring patient confidentiality, and maintaining a balance between technological advancements and ethical standards. 

Further Reading:

  1. Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10-18.
  2. IBM. What is artificial intelligence (AI)? [Internet]. IBM. IBM; 2023. Accessed 2023 Nov 26. Available from: