This paper is a general summary of Neuralink, a revolutionary technology set to elevate human life and neurology. Neuralink itself is a key testimonial to the evolution of neuroscience and even brain-computer interfaces, otherwise known as BCI. The original few BCI experiments were conducted on monkeys in the 1960s and 70s, in which the experiment itself narrowed down and understood brain function as a general concept [3]. More specifically, "Work on these technologies began in the early 1970s, led by computer science professor J.J. Vidal at UCLA" [12]. Science itself progresses day by day, growing rapidly in recent years, especially in neuroscience, something highlighted as a focal point in the previous statement. Moreover, recently we have seen technology go on a rampant rise in terms of popularity, inventions, and changes to the human lifestyle. The interactions humans had with technology initially developed with wearables or wearable technology, such as Apple Watches, AirPods, and Fitbits, and now they have even prompted advancements in brain-computer interfaces. Technology has had the power to advance science, but now it’s capable of changing the human mind. Going back to Neuralink, it’s a startup that began its initiative in 2016 and was approved by the FDA for clinical trials in May of 2023, ready to create a wave of change in the field of neuroscience [6]. The foremost baffling thing is how this chip plans on being placed in the somatosensory system. The somatosensory system is a part of the brain that deals with motor actions, recognition, and perception, and applying Neuralink in this area should supposedly allow for cures and treatment of amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injuries, epilepsy, autism, depression, schizophrenia, and possibly blindness [9]. Neuralink is deemed to lead to a life-changing future, and with co-founders and investors like Elon Musk, there is a lot to know about this piece of technology.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].