**2.3 AI in ophthalmology**

In ophthalmology AI is tremendously used in detection and monitoring of diabetic retinopathy [28–33], glaucoma [29, 34, 35], age-related macular degeneration (AMD) [29, 36, 37], retinopathy of prematurity (ROP) [38] with the help of retinal cameras of fundus photography [16]. The diagnosis of ocular diseases is based on the deep learning system that is trained on the numerous images of each disease. A study of the population with diabetes from US, Australia, Europe, and Asia in the years between 1980 to 2008 shows the frequency of diabetic retinopathy of 34.6% and 7% vision-threatening diabetic retinopathy [39]. Thus**,** continuous monitoring along

**Figure 2.** *Clinical radiology workflow.*

**Figure 3.**

*Use of AI in diabetic retinopathy diagnosis and treatment.*

with the treatment can prevent vision loss. A schematic representation of the use of AI in ophthalmology is depicted in **Figure 3** [40].

Machine learning models such as visual fields, optical coherence tomography (OCT), and optic disc characters are used for the diagnosis of glaucoma [41]. Glaucoma is a medical condition in which the intraocular pressure inside the eye rises up. Age-related macular degeneration (AMD) is a condition in which there is degeneration in the center of the retina and is responsible for vision loss. Spectral OCT is used in the diagnosis of AMD [42]. Retinopathy of Prematurity (ROP) is a disease that occurs in premature born babies and the leading cause of childhood blindness due to abnormal growth of blood vessels towards the edge of retina. Wide-angle retinal images with machine learning [43] and i-ROP DL system which works on the basis of convolutional neural network (CNN) was trained on the images more than 5000 in number with a single standard reference diagnosis (RSD) [44] for the diagnosis of ROP.

*Artificial Intelligence in Healthcare: An Overview DOI: http://dx.doi.org/10.5772/intechopen.102768*

**Figure 4.** *Biopharmaceutical companies using AI technology in drug discovery.*

### **2.4 AI in drug discovery**

In this modern era, AI is used for drug discovery and drug design on the basis of artificial neural network (ANN), algorithm, and deep learning. In drug discovery, inaugural employment of ANN was in 1970 to detect whether the 1,3-dioxane is physiologically active or not [45]. The application of ANN in Quantity Structure Activity Relationship (QSAR) is the next stage in the field of drug discovery [46]. QSAR studies were involved in drug design since 1960 by involving the simple structures to know the activity of the combination of compounds [47]. Currently the biological and physicochemical activity i.e. ADMET (Absorption, distribution, metabolism, excretion, and toxicity), binding constants according to their binding sites are also vaticinated using ANN which is trained on various sets of compounds in the field of drug discovery [48]. The application of AI is at every step of the drug discovery process, from identification of drug targets to new drug molecule research following its volunteer election for clinical trials [49–51] also its pharmacological property [52], its binding effect with protein, potency and synergistic effect with other drugs [53, 54]. Docking software which is used to find the perfect binding molecule for the particular receptor and its activity also works on the algorithm. AI has simplified the process of drug discovery by saving time and money expenditure of US\$2.5 billion on R&D [55]. Thus, AI has routed the drug discovery process into simpler, quicker, and cost-effective**,** an example of drug discovery is by BenevolentBio which has its own AI platform and was asked to suggest the treatment of amyotrophic lateral sclerosis (ALS) also called as motor neuron diseases (MND) and has displayed nearly 100 of drugs, five drugs were selected out of which four of them were effective and one was showing delayed neurological symptoms on mice [56]. The top multinational biopharmaceutical companies have started using AI technology in their drug discovery (**Figure 4**) [57].

#### **3. Conclusion**

Artificial intelligence is an important and valuable technology that offers promising solutions to healthcare industry needs. It opens up gateways to individualized treatment approaches tailored to the needs of individual patients. It offers multiple advantages over traditional analytics and other clinical decision-making tools. Data becomes more precise and accurate allowing the healthcare industry to have more insights into the diagnosis and treatment processes thereby improving patient outcomes.
