**3.2 Head, eyes, ears, nose, throat (HEENT)**

Glaucoma, an increase of intraocular pressure that results in optic nerve damage and ultimately blindness, can be diagnosed with AI. Through the proliferation of a neural network, retinal images have been mined to aid in in diagnosis of Glaucoma with up to 96% accuracy [13].

Diabetic retinopathy (DR), a common microvascular problem of diabetes, is also a significant source of irreparable loss of sight [14]. This disease and subsequent loss of vision can be averted and assorted therapeutic selections are obtainable. Despite calls for routine screening for DR comprehensive strategies face difficulty with implementation [15]. Implementation issues include: inadequate trained personnel, lack of resources, and inability to cope with an increased disease burden. To combat this concern, a deep-based learning algorithm was created to validate the detection of DR [16]. Retinal images were compared to that of trained ophthalmologists. Results showed high accuracy when compared to current standards of care, which may lead to more efficient and accessible screening for DR.

### **3.3 Cardiovascular**

Cardiovascular disease (CVD), the foremost cause of illness and death globally, consumes extensive preventative measures to curtail risk factors for disease development that center around controlling hypertension, lowering cholesterol, smoking cessation, and optimizing diabetes management. Including age, risk factors for development of CVD are mainly predicted using validated instruments [17–20]. Nevertheless, many people are still at risk for the development of CVD and are unable to be identified with these tools. What's more approximately 50% of myocardial infarctions and strokes will occur in people that do not meet screening criteria and thus are considered to be low risk [21]. Fortunately, machine learning provides a chance to expand precision by taking advantage of multifaceted connections among risk factors. For example, in a prospective cohort study machine learning correctly predicted additional individuals who got CVD versus a standard set of rules [22]. These results show that ML may identify more individuals who might be helped from anticipatory therapy and help others eschew pointless therapy.

### **3.4 Gastrointestinal (GI)**

Gastro-esophageal reflux disease (GERD) is the presence esophageal mucosal interruptions or occurrence of reflux-induced symptoms that significantly impairs quality of life [23]. Symptom evaluation and assessment is vital for disease management. Sadly, symptom evaluation and effects of reflux are currently insufficiently correlated with disease severity. Furthermore, given the ambiguity of these relationships no diagnostic tool remains reliable. A retrospective study of 150 patients compared AI in the form of an artificial neural network (ANN) comprised of 45 clinical variables versus the current standards to esophagoscopy or pH-metry.

The use of ANN to make a diagnosis of GERD demonstrated superior accuracy [24]. Although this work is still in the preliminary stages it shows promise in delivering a non-invasive approach to the diagnosis of GERD.
