number of patients are responding to these kinds of treatments. Currently, a precise machine learning algorithm is developed to classify and identify the capability of the natural body immune system to treat cancer. AI is also supporting to enable virtual biopsies in the field of radiology, which achieves harnessing image-based algorithms to characterize the patterns and genetic structures of tumors.
AI could reduce the impact on shortages of well-trained healthcare providers by handling over some diagnostic tasks which are allocated to humans. An AI imaging tool take X-rays for tuberculosis symptoms with better accuracy compared to the human. This algorithm is diversified, which considers the environmental factors influencing the disease.
1.2.5 AI in the Monitoring Process
Smartphones have provided numerous tools for patients, which are extremely useful for extracting, transferring, and processing health information. Machine learning algorithms develop mobile applications for analyzing child facial diseases. These algorithms detect unique features such as the child’s jawline, eye, and nose placement to identify the craniofacial abnormalities.
Tracking health information is extended away from hospitals through wearable devices to maintain the fitness of the patients. Wearable devices are made up of internal sensors to measure and save health parameters. AI is used for extracting and analyzing the vast data from the devices and generates useful insights.
1.2.6 Challenges of AI in Healthcare
The potential of AI to increase access to healthcare is tremendous. However, sensible information of the patients is used for processing, which must be protected with restricted access. AI is a system developed by humans; it should have the capability of discriminating features to secure the information. Now, the technology exists everywhere, which causes an impact on human health. It is also required to maintain the balance in using techniques in healthcare.
The essential applications of AI in healthcare are summarized in Table 1.2.
1.3 AI-Driven mHealth Communication System and Services
AI aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and the rapid progress of analytics techniques [12–14]. Recently, AI techniques are applied in mHealth services and systems. There are various health-oriented smartphone applications available. There are around 160,000 of them, which are downloaded about 660 million times [15]. Moreover, blood pressure and heart rhythm are a few smartphone-connected devices that enable remote assessment of health conditions [15]. Identifying atrial fibrillation is one of the hottest topics in the field. The detection of atrial fibrillation was carried by comparing smartwatch data with around normal ECG data of 9,750 patients [16]. A deep learning algorithm combined with smartwatch applications exhibited excellent forecasting of atrial fibrillation with a specificity of 90.2% and a sensitivity of 98%.
Table 1.2 Applications of AI in healthcare.
| Source | Subject matter | Parameter analyzed/considered | Related performance measures |
| [9] | AI in Healthcare | Brain Computer Interfaces (BCI)Next generation radiology toolsExpansion of AI-healthcare networkElectronic Health Record (EHR)Antibiotic ResistancePathology analysisIntelligent medical machinesImmunotherapyRisk predictorHealth monitoring systemDiagnostic toolsClinical decision-making | BCI improves quality of life for patients with ALS, strokes and 5 lakhs people with spinal card injuries with every yearVirtual biopsies characterize the phenotypes and genetic properties of tumorsVoice recognition and dictation help in Clinical documentation.75% EHR use as a tool for right diagnosisAI-based Risk scoring and stratification toolsDL identifies novel connections between seemingly unrelated data sets |
| [10] | Natural language processing | Virtual AssistantMelaFindRobotics Assisted Therapy | Helps the patients with Alzheimer’s diseaseDiagnose tool to analyze irregular moles melanoma skin cancerAssists the patients during stroke recovery |
| [11] | Biological intelligence | Medical data miningANN-based predictionAI-Clinical decision-making | Diagnoses orthopedic trauma from radiographs10% more accurate than conventional decision |
1.3.1 Embedding of Handheld Imaging Platforms With mHealth Devices
Authors Bhavani et al. organized the integration of pocket-size ultrasound (POCUS) with mHealth devices to deal with heart diseases. In the reporting, there were around a total of 253 patients with heart diseases, which are randomized into two groups of mHealth clinics and standard healthcare. The pocket-size echocardiography was used from remote on medical decision-making with patients who have valvular heart diseases. The mHealth devices are associated with minimum referral time for intervention and improved probability of intervention in comparison with standard healthcare. mHealth also decreases the death rate and hospitalization. In work, the authors have embedded POCUS with mHealth devices and verified that the embedding could be performed in previous medical clinics with the required clinical outcome.
1.3.2 The Adaptability of POCUS in Telemedicine
Thus, POCUS has enabled point-of-care screening to resource-limited communities. The screening is associated with cardiovascular diseases. Moreover, the scanning of 1,023 studies was done in a remote place whose images were sent to the physicians for review through internet-based platforms. The sending and reviewing process was completed with the median time of 11:44 h. This study has proved that the remote assessment of echocardiographic has an added value of using internet-based assessment for cardiac illnesses.
The interpretation of images from remote was tested in a smartphone. Around 83 patients images were sent to remote locations and tested in smartphone applications. It is observed that the non-expert diagnosis was revised by remote experts.
It is also possible to apply deep learning and machine learning in the analysis of echocardiography. It is shown that the algorithms like random forests and support vector machines could distinguish between hypertrophic cardiomyopathy and athlete heart more precisely than any traditional measures. Moreover, supervised learning approaches with the required number of ECG variables verified the superiority of machine learning algorithms by distinguishing between restrictive cardiomyopathy and constrictive pericarditis [17].
The usage of deep learning algorithms for image classification is very obvious. These kinds of applications are useful in computer vision. The algorithms can recognize the patterns in heterogeneous syndromes and cardiovascular images. The deep learning methods can improve the accuracy of 2D STE and other modalities of imaging [18, 19]. The idea can be extended to other imaging modalities, namely, 3D STE and cardiac image of magnetic resonance imaging kind. The deep learning algorithms work well even in noisy data, namely, strain imaging. The diseases like Takotsubo cardiomyopathy, hypertension, Brugada syndrome, heart failure, and atrial fibrillation
