Healthcare industry is at the threshold of AI revolution. From aiding in disease diagnosis and treatment planning to streamline administrative tasks, AI offers immense potential to revolutionize healthcare industry, as indicated by the research findings of Accenture, which highlights that more than 84% of the healthcare professional believes in AI revolutionizing the healthcare industry in coming years.

AI has the potential to improve both the quality and accessibility of healthcare. This is also why the global market of Artificial intelligence in healthcare industry is expected to reach $260 billion USD in the coming two years. 

Today more than 86% of healthcare providers, life science companies, and tech vendors reported using AI in 2023. This statistic, according to a report by Binariks, highlights the significant penetration of AI across various segments of the healthcare sector. However, most of these organizations are void of AI governing regulations. 

The Brookings institute report from 2023 brought forward concerning and insightful findings, highlighting that only a handful of countries have implemented comprehensive regulations for AI in healthcare. 

Another report by WHO points lack of AI governing policies as the most crucial and vital challenge for the healthcare industry. 

Through this article, we aim to bring forth the uneven development in the Ai implementation and adoption strategies, simultaneously highlighting the urgent need for robust infrastructure in AI Implementation.

AI’s Penetration in Healthcare Industry:

The recent COVID-19 pandemic addressed the elephant in the room- the lack of the funding and research in healthcare industry. The pandemic brought global medical industry to its knees, crippling with lack of staff and funding.

The pandemic also highlighted industry’s growing need of innovation and quick response time to the deadly virus. This brought the reality of healthcare system to forefront, where medical sector not only lacked money, resources, and direction, the medical sector was also void of the latest technology- the Artificial intelligence. 

Although AI was not completely new addition in the industry, the pandemic definitely pushed its integration in the medical industry. 

The pandemic led to a massive influx of healthcare data, including patient records, imaging data, and research data. This data proved crucial for training and improving AI algorithms in diagnostics and drug discovery processes. The pandemic also exposed the pressure points within healthcare systems around the world. In these cases, AI solutions became an easier way to automate medical tasks and improve worker’s productivity. 

After the pandemic, more than 73% companies increased their AI funding during and after the pandemic. The organizations also realized the benefits of integrating AI in effective monitoring of COVID cases, vaccine development, and distribution. 

This helped AI in penetrating in medical industry on broader level. Prior to this, AI was limited to research and medicine development. Acting as a catalyst, pandemic generalized and broadened the applications of AI in medical industry, 

The recent development of Neuralink and its successful trial on first human participant is another step towards deepening the relationship between AI and medical sciences.

While AI is charging forward in healthcare, regulations are lagging far behind, creating an alarming disparity. This is the main cause of concern for not only global scientists, but also healthcare professionals, companies and individuals. 

AI is Fueled by Large Medical Records of the Patients: 

The most important aspect of the use of artificial intelligence in healthcare industry is its training on large datasets of human medical records. Human medical records contain patient histories, clinical trials, medical imaging and other identifying data on patients.

This data fuels Ai algorithms, which are capable of identifying trends and patterns in patients’ medical records. For instance, A 2023 study published in Nature Medicine demonstrated how AI trained on retinal scans could detect signs of diabetic retinopathy even before symptoms appear. 

Another study by JAMA internal Medicine found that AI algorithms outperformed humans in tailoring medicines for heart failure patients. AI is also employed for drug discovery, performing minimal invasion surgery, and even for providing mental-health support for patients. 

All these instances demonstrate the advantages of employing AI in medicine. However, training AI is heavily dependent on medical records of the patients, whose ethical sourcing and Anonymization still remains a great concern for health professionals around the world. 

Additionally, AI algorithms are susceptible to inheriting biases present in the data sets used to train them. This can lead to unfair and discriminatory practices and results. For example, a study published in Science Translational Medicine in 2019 found that an AI-based algorithm used to predict risk of recidivism in the criminal justice system exhibited racial bias.

Furthermore, determining accountability and liability for AI-related errors poses another ethical challenge. In the absence of clear legal frameworks, it's unclear who is responsible for such errors.


Hence, it is imperative to ensure that the ethical concerns about AI’s application in healthcare are mitigated, so that its potential benefits are fully realized and not overshadowed by shortcomings. To achieve so, we need to address the concerns in data privacy, accountability, confidentiality, algorithmic bias and data security. 

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