**4. Future of artificial intelligence in medical sciences**

Medical science enfolds various courses which describe the anatomy and functions of human body. Basic biology like anatomy, physiology, and biochemistry with many other graduate subjects in medicine come under medical sciences. Today, AI technology and machine learning (ML) have developed ahead of biological sciences to apply on a vast majority of medical specialties such as radiology, screening, psychiatry, primary medical care, diagnosis of the disease and telemedicine [17, 18].

Future of AI in medical sciences can be discussed in three forms (**Table 3**).


### **4.1 AI in medical education**

Curriculum of medical education now emphasize more on e-learning methods which are yet to adopt in many countries. AI helps to see the bridge between the availability of digital resources and utilization of the resources by medical students and teachers. Integration of many technologies akin to neural networks, expertise, deep learning, machine learning, speech, image, and language recognition simulate insightful behavior of humans. Lately, AI has gained vast application in medical education. Many research has been conducted to observe the cause of underutilization of e-content by the students and teachers to reach the conclusion for possible solutions of those problems [9].

Presumably, AI will help teachers to promote the students for self-directed learning and will help to give healthy discussions on case-based studies. The major hurdle is to face multiple distractions while finding knowledge about simple and small topics by the students through e-learning platforms. In near future, as the technology advances, there might be a possibility of level wise distribution of digital


**Table 3.**

*Future of artificial intelligence in medical sciences.*

content based on the understanding of graduate and post graduate students, as well as researcher and scientists. This distribution of content in digital library will be time saving for the teachers, students as well as for researchers [19].

Moreover, standard and quality along with the accessibility of the content will be considered the double edge sword for any digital content to make it available for the students. Digital files will be more consumer friendly whether for teachers or for medical students, filling the gap between physical and digital resources. AI can be used to monitor the efficiency of e-resources to be used by the consumer on frequent basis and enhances the scope of improvement in medical education system globally in the universal form. It can make a synchronized understanding of the subjects between students and the medical faculty [20].

As the teaching is always followed by the assessment of students for different subjects, development of various digital platforms will make the assessment methods more convenient and user friendly. These platforms will save much time for the assessment as compared to the conventional methods. It will make formative evaluation easier for the teachers. Students can also evaluate themselves on different steps of learning by the newly developed assessment methods, giving them more confidence for development in the correct direction during their stay in medical schools. A digital self-assessment program can be developed for the students to judge themselves as to where they stand overall throughout the medical studies. These types of assessments will be helpful and time saving for the challenging newly applied competency based medical education curriculum, which is promising for creating competent physicians and surgeons to embark in health care system globally [21, 22].

### **4.2 AI in medical research and innovations**

Implementation of machine learning (ML) to expedite clinical exploration are sporadically discussed on intellectual ground. Medical research is an extensive field, with investigations and observational evaluation, guiding traditional trials with realistic elements which in turn encourage clinical registries and additional implementation work. Clinical research is invaluable to improve the health care and outcomes. It has been proved as complicated, demanding in terms of labor, expensive and vulnerable to unexpected errors. ML has the possibility to help and improve the accomplishments, universality, patient focusing and effectivity of clinical trials, preventing the loss of years as well as dollars of expenditure as have been done in many conventional settings of analysis [10, 23].

Functional and metaphysical barriers in ML can do well in clinical research in future after précised focus on them. The prospective applications of ML to medical research recently overtake its existing use, because few potential studies are available about the reasonable effectiveness of ML in contrast to the conventional approaches. Conversion of traditional methodology to ML needs time, enthusiasm, and collaboration for effective adoption. Communication and cooperation are crucial for application of this favorable technology for the future application in medical research and innovations [24].

The future goal for application of ML in research is to create fair and ethical innovations that will be universally acceptable. Vigorous and integrative collaborations can reduce chances of bias in clinical research with ML. More diverse teams may offer innovative insights for de-biasing ML models [2].

#### **4.3 AI in diagnosis and treatment of diseases**

For healthcare delivery of the future AI has a very important role. At the beginning, efforts to provide diagnosis and treatment are challenging but expectations to pick up in this area is anticipated in the future (**Table 2**). In radiology and pathology
