Table 4.

Advantages and limitations of existing studies employing Artificial Intelligence and Machine Learning in healthcare.

Field of Study	References	Advantages	Limitations and Future Research Directions
Robotic Surgeries	[86,88,92,93,95,97,99,100,101,102,103,104]	Total operational time was reduced by approx. 50%; accuracy of performing minute tasks such as stitching and knotting, improved up to 90%; probability of post-surgery infections, decreased up to 30%; up to 77% blood losses can be prevented; total operational cost reduced up to 35%.	Most of the techniques are designed to achieve maximum precision and do not take patient comfort into attention; the cost of initial setup and maintenance is high; training methodologies for healthcare staff still need to be well explored.
Disease monitoring	[105,108,109,110]	Continuous monitoring can prevent hazardous situations; fine-tuning of on-going treatment is possible; total duration from getting sick to get cured reduces up-to 40%; better treatments can be planned; research and development of medicines can be accelerated.	Existing studies mainly focus on case-wise studies while generalized methodology can be an area of interest for researchers; mixed datasets scenario can be an exciting area as a future work; present study covers most frequent diseases while there is various disease where AI can be utilized; home-based monitoring is not addressed in these studies.
Diagnosis and prognosis	[106,108,111,112,113,114,115,116]	Initial process time can be reduced up to 70%; early prediction can prevent worse conditions; predicted prognosis and expert suggestions can be fused to produce best possible results.	Comparatively high false positive ratio; high miss-classification ratio; extrapolation accuracy still needs to be better than an interpolation; integration with existing technology can be a matter of further studies.
Prediction systems	[107,117,118,119]	Early predictions can help doctors to provide better treatment; intelligent phone-based prediction systems help patients to know their condition without visiting the hospital; can be used to find the root cause of any disease.	High false positive ratio; requires many data for training; high computational power is required for training and prediction; technology is comparatively costly.