## 5 Challenges and Benefits of AI
### Benefits and Challenges of Artificial Intelligence in Healthcare
#### 5.1.2 Challenges  
AI is projected to soon be incorporated into routine clinical care due to its proven efficacy in refining the administrative aspect of health services. However, ethical and privacy implications are reservations that have been expressed about introducing AI into the healthcare system. These reservations include AI application tasks in clinical situations, the fuzziness of some AI algorithms, privacy concerns for the data used in training the AI model, the likelihood of bias and security concerns. Similarly, access, consent, costs, efficacy, information, the right to decide and the right to try are some examples of the ethical concerns faced in clinical applications of AI techniques [107; 109]. AI application is the preferable and right approach for progress in the health sector, particularly health services, despite its impact on ethical concerns, regulations, and systems error. The following are some of the open challenges of AI in healthcare. 1. Equity  
Clinical datasets for training AI models should be well-structured and in an appropriate format that can make it easy to derive knowledge and actionable insights from it. That is, there should be adequate representations of instances in the datasets via the harmonization of different health-related data of patients [110]. Otherwise, this can cause the AI models to be biased, make imprecise predictions or even large-scale discrimination [111]. 2. Transparency  
Although the performance of the AI techniques, especially DL models in the prediction of clinical risk and medical image analysis has been significantly promising, nevertheless explaining and interpreting the DL models is difficult. This difficulty is a cause of serious concern in the medical world, where the ability to explain medical decisions and transparency are very vital [111; 112]. 3. Trust  
To apply AI, matters including the cause and effect of a disease, the ML techniques and the models used to support the decision-making process of the medical experts needs to be considered by clinicians. The future of AI applications' autonomous roles and the conceivable exposure to the accidental or mischievous tampering of these applications to yield unjustifiable results may cause a critical obstacle for clinicians to admit AI in their clinical practice [111; 113]. 4. Accountability  
The accountability begins with AI model development and then extends to the level where the model is used in medical care until eventual retirement. Many stakeholders are involved in this scope, which includes software developers, healthcare experts, patient advocacy groups and government officials [114]. The application of AI in healthcare services is not limited to increasing clinical capability but can also be for upgrading administrative capacity. As an example, the distribution of information and services related to health can be conducted using AI in the form of telemedicine by employing communication technology. Certainly, the implementation of telemedicine will impressively affect business models in hospitals [115]. 5.2. Problems in the Application of AI in Health Services  
1. Bias data: Large-scale data inputs related to clinical health records are a requirement for training or developing a robust AI model. Otherwise, bias can occur when incomplete or insufficient health-related data are used for training the AI models. Likewise, the output from training data that do not truly reflect the target population is usually biased. Under-representative data may occur as a result of accessibility to healthcare services (social discrimination) and/or comparatively lesser samples (such as with data from minority groups) [116]. Privacy: Medical service data are one of the most sensitive records that can be kept by an individual about another. Therefore, the principle of respecting the privacy of a patient is of vital importance in the medical care profession. This is also because privacy is bounded by the autonomy of the patient's well-being and a private identity [111].