Artificial intelligence in ophthalmology - Machines think!

Abstract

Can Machines Think? “Artificial intelligence is one of the most profound things we’re working on as humanity. It is more profound than fire or electricity.” - Sundar Pichai It was 72-years-ago that the British mathematician Alan Turing had posed a provocative question “Can machines think?” in his landmark 1950 paper “Computing Machinery and Intelligence”. He also described a test to evaluate the humanness of the computer.[1] The Turing Test is a 3-player imitation game in which a computer tries to fool a human interrogator into thinking that it’s a person.[1] Seven decades later, despite all the advances in artificial intelligence (AI), no computer has conclusively passed the Turing Test. Despite the failure to conquer the bar of artificial general intelligence prescribed by Turing, computers have come to become an inseparable part of our lives and times. Perhaps, AI is not meant to evolve like a human mind. As Stuart Russell and Peter Norvig pointed out, “Aeronautical engineering texts do not define the goal of their field as making machines that fly so exactly like pigeons that they can fool other pigeons.” The evolution of AI has opened yet unfathomable new paradigms in several domains that intricately affect us, including medicine. The Bloom Beyond the Dark AI Winter Unimate, the first industrial robot arm, Eliza, the natural language processor to mimic human communication, and Shakey, the first electronic person were the significant milestones in the early evolution of AI from 1950 to 1970.[2] The period 1970-2000 is referred to as AI winter when skepticism and the prohibitive cost leashed the rapid development of AI.[2] Despite the general winter, the application of AI in medicine saw some progress in these years. A consultation program for glaucoma based on casual-associational network (CASNET), MYCIN, and EMYCIN which guided decision-making for antibiotics and DXplain to generate symptoms-based differential diagnosis evolved during this period.[2] Advances in computational power, newer learning algorithms, availability of massive computer-readable data from electronic medical records, wearable health devices, accessible funding, and the positive influence of the passionate thought leaders have resulted in a seminal transformation in AI and systematic exploitation of the potential of machine learning (ML) in the last two decades. There are essentially three types of AI – artificial narrow intelligence (weak AI), artificial general intelligence, and artificial superintelligence.[3] Based on likeness to the human mind, AI and AI-enabled devices can be classified as 1. reactive machines, 2. limited memory machines, 3. theory of mind AI, and 4. self-aware AI.[3] AI has itself evolved into several unique but inter-connected subfields [Fig. 1]. The domain of ML has radically expanded to include deep learning (DL) and neural networks [Fig. 2].Figure 1: Various domains of artificial intelligenceFigure 2: Deep learning is a type of machine learning. A convolutional neural network is a variant of deep learningConvolutional neural network (CNN) is an advanced multilayered variant of DL, which simulates interconnected neurons of the human brain to analyze an input image to recognize patterns.[2] Le-NET, AlexNet, VGG, GoogLeNet, and ResNet are some of the CNN algorithms.[2] IBM Watson was developed to answer the quiz show Jeopardy!. In 2011, it competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the prize of $1 million. It is based on DeepQA (which “deeply analyzes natural language input to better find, synthesize, deliver, and organize relevant answers and their justifications”)[4] and leads the pack in clinical applications of AI. Watson Sugar. IQ helps optimize diabetes management.[4] Watson for Oncology (WFO) supports 12 common cancers accounting for 80% of overall cancer incidence. WFO, together with Watson for Genomics, Watson Clinical Trials Matching, and Watson for Drug Discovery, is helping to make