*Types of Artificial Intelligence and Future of Artificial Intelligence in Medical Sciences DOI: http://dx.doi.org/10.5772/intechopen.112056*

most images will be analyzed and examined by the machines at some point. Usage of already working speech recognition and text identification for communication with the patients and getting clinical notes will increase [11]. A widespread challenge for the use of AI in health domain is the ensured adoption in clinical practices rather than proven capability of technology itself. This provocation can overcome by integration with the system, approval by the regulators, sufficient standardization, awareness to the clinicians and getting updated (both medical professionals and consumers) over time-to-time basis. Overcoming the challenges will take longer time compared to the time taken by technologies to mature [12].

There will be more use of technologies in next 10 years but not within 5 years due to this time constraints in adoption of something new in medical field. On a substantial scale, it is very clear that AI methods will not supersede the human physicians, but rather will boost their endeavors for patient's care. Human physicians gradually may proceed towards the job motif that makes them capable on unique soft skills like empathy, and the integration with unique understanding on the big scale. Conceivably, the healthcare personnel who deny to work next to the artificial intelligence will no longer have a job in near future [12].

It is important to consider the development of our health care systems in terms of AI. These technologies potentially transform various aspects of patient care better than humans, most importantly the diagnosis of disease. But replacing humans by computer's AI for a vast medical domain will take many years due to multiple barriers [25]. To achieve the human level performance in terms of cognition, intelligent behavior of a computer has been used since year 2016, a well-known time to show highest investments in AI for healthcare applications [26].

As we already are familiar with "virtual" and "physical" subtypes of AI [8]. The physical part deals with the performance of robots in various surgeries, care of handicapped individuals and elderly people. The virtual part deals with a range of information data from digital records of the patient's health to the guided neural network in treatment decisions of the patients. It describes the diagnosis of the patients via two wide techniques: Flowchart based and Database [2].

The flowchart-based method translates the sequence of questions of a physician for taking history to reach a most likely diagnosis after amalgamation of complex presented symptoms. A large amount of data, containing multidirectional clinical features of diseases, is the main requirement into cloud-based machinery networks. A major challenge in ML is inability to gather patient's cues which can only be observed directly by a doctor during consultation. This results in a belief that AI can assist the physicians in future but cannot replace the human physicians in health care [2].

The database uses recognition of different images of a specific group to apply for answering the questions related to a particular diagnosis. A decade ago, google project "artificial brain" was designed on the principle of deep learning by database approach. This approach was used to match and mismatch various images in radiology and pathology for diagnosis of distinct sets of diseases [2].

MYCIN, Watson and some free open source such as Tensor Flow on Google are systems developed to incorporate in healthcare system. The strict rule oriented clinical opinion making machinery systems are not easy to maintain on medical ground due to constant change in medical knowledge. A big amount of data handling too is a big challenge for the healthcare system in ML. Statistically based ML framework leading the way in a period of evidence-based medicine, which is reflecting a positive change in broad term, but has many challenges such as ethical issues of the patients. Google now a days collaborates with health delivery channels to make prediction

designs from big data to alert the physicians for high-risk situations, like sepsis and heart failure [12].

Various firms are also there to focus more on investigation and treatment protocols of different cancers based on their genetic outlines. Foundation medicine and Flatiron health are specialized firms for complex understanding of all the genetic variants of cancers and their response to new treatment protocols. These rules-based, algorithmic diagnosis and treatment methods are many times challenging to get embedded in clinical fields. Majority of AI techniques address only one aspect of medical care thus standalone in nature. Such incorporation issues have possibly been a substantial barrier to broaden the application of AI than accuracy and effectiveness of the technique itself [27].

Patient's cooperation is the final need for making any method to give good or bad outcomes. Better outcome has been observed as the participation of patients increases when they become more active to owe well-being and good health. For the better health outcomes, AI will be developed in such a way which personalize and contextualize the care. This can be supplemented by message alerts and provocative actions for the concerned patients [12].

Administration uses the AI less potentially, but it provides substantial efficiency in management of revenue cycle, clinical notes, claims processing and medical records documentation. False insurance claims can be identified easily and help the health insurers and governments to save time, finance, and lot of efforts of stakeholders [12].

To the best of all outcomes by using AI, it is believed that no jobs will be eliminated in health care working in parallel with the AI. Jobs pertaining to the direct patient interaction will have less impact to fade itself. In AI systems, radiology and pathology perform a single task such as specific nodule detection in chest computed tomography and specific specimen findings in a biopsy result. Only a few of pathology and radiology findings have been identified by AI till date, thus showing the role of human pathologist and radiologist to be there for a longer time before technology fully replace all the possible tasks done by the medical specialists. It is likely to create more jobs for the individuals having knowledge to work with AI which can further develop the effective use of AI in future [11].

In public health area, AI has a well-established role, which causes reduction in time of the doctors given on diseases already observed and treated many times, augmenting their work on more complicated and rare cases. Reshaping of various aspects of medical services are possible by these developing technologies and many patients can take advantages of taking alternative medications and follow-up care without much efforts. AI is expanding to have a significant impact on every angle of primary health care, reducing physician's labor and increasing their efficiency, precision, and effectivity. But AI cannot replace medical experts completely in the tactful branch of mankind [2].
