**5. Limitations of artificial intelligence in medical sciences**

Data availability for construction of well executed artificially intelligent models consult the large quantities of high-quality data. Patient's confidentiality and public right to privacy issues restrict the data availability [14–16, 24], making compromised framework with limited potential (**Table 4**). This fragmented data limits the predictability of a model for successful application of AI within and between the organization. Biased data processing with or without biased data collection in terms of population specificity for distinct race, age, and gender result in the distorted

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


#### **Table 4.**

*Limitations of artificial intelligence in medical sciences.*

collection of data, fabricating defective algorithm. Thus, it is invariably difficult to find elite algorithm matched for upcoming task to accomplish. Basic information is constantly needed to understand, for the building of AI prototypes, by a user. These details help them to interpret the correct or incorrect output and execution of preferable use of the output. But, despite having some latest studies in this direction, complex black boxes of mathematical algorithms are burdensome to approach and decipher precisely by the medical users [24].

Machines can be able to construe human behavior, but many human characteristics such as rational thinking, interactive and social skills, emotional understanding, and ingenuity cannot be acuminated by the machines and robots. The qualities for humanity present in the doctors cannot be replaced absolutely by AI. It is required for the medical neophyte to learn the notions and relevance of AI and how to ramify well organized work along with machines for greater advantages alongside plowing soft skills in them [8]. A wide range of skills are needed in future physicians to accommodate the constant changing technology-based healthcare delivery. An adequate understanding of technical concepts, basics of AI, data management and treatment oriented ethical issues are some newer expertise to incorporate in upcoming medical generations apart from the mastering medicine. These abilities will equip the doctors to identify the accuracy of machines, reducing the chances of error. Thus, a supervisor of AI tools will always be needed even with a well-established source of treatment modality and robots [28, 29].
