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AI in Healthcare: Data Protection Challenges

Through computer science, in reference to human intellectual ability, Artificial Intelligence (AI), also called Machine Intelligence is intelligence exhibited by computers. Founded in 1956 as an academic field, Artificial Intelligence has undergone many waves of excitement over the years, accompanied by frustration and loss of funding (known as “AI winter”), accompanied by alternative approaches, progress, and revived investment.

AI research’s typical concerns (or objectives) include inference, representation of information, preparation, reading, processing of natural language, perception, and the ability to move and process information.

AI research’s conventional challenges (or goals) include reasoning, representation of information, scheduling, training, interpretation of natural language, awareness, and the ability to shift and avoid obstacles. General intelligence is one of the lengthy-term priorities of the sector. The strategies include numerical, mathematical, and conventional conceptual AI approaches.

AI in healthcare

Artificial Intelligence’s ultimate research goal is to create technology that enables computers and machines to operate intelligently.

Descriptive AI

Descriptive AI is probably the most frequently used in biomedical innovation and has the most promising quick-term potential. It takes into consideration incidents that have already taken place and uses this information to gain more insight, such as identifying patterns and subtle changes that might otherwise be prevented by healthcare professionals.

Predictive AI

Predictive AI uses detailed data to try to foresee the future. Medical professionals use AI to provide information and recommend behaviours in a proactive manner. AI may play a vital role in predictive healthcare and hospital administration developments.

Prescriptive AI

Prescriptive AI fosters the intent of predictive AI, not only identifying patterns which may not be anticipated by individuals but also recommending potential clinical nuances-based treatment. This ability to create decisions makes prescriptive AI in the near term the most fascinating and contentious use case.


For medicine, there are growing magnitudes of Artificial Intelligence. AI can help in the creation of online services that allow physicians and practitioners to access thousands of therapeutic tools within the blink of an eye. In terms of efficiency, AI can assist a physician, offering both quantitative and qualitative statistics based on feedback, enhancing early diagnosis, precision in treatment and estimation of the outcome. AI’s capacity to “learn” from the information offers an opportunity to enhance effectiveness based on input responses. Medical care AI programs also work in real-time, which ensures that the information is constantly updated, increasing reliability and significance. Physicians have nearly unlimited resources to achieve their care capacity with the collection of continuously updated data.



Accessibility to detail is the limitation of AI in healthcare implementation. The primary data concerns involved in gaining consent and making the information secure and accurate.


The need for traditional design requirements for proposed AI systems has now been discussed by industry experts. Standards for different models can help to provide frameworks to ensure the AI approach towards privacy, security, performance and accuracy and address issues of ethics as well as trust.


The introduction and acceptance of AI pose a number of challenges. Some of these include the following:

  • Regulatory Authority: — Medical councils’ authority to regulate the clinical aspects, and a data privacy bill regulator to monitor data issues is required for effective solutions.
  • Infrastructure: — For instance, cloud computing architecture is mainly focused on outside servers. Delays in spending on indigenous resources lead to better access to technology and research for start-ups outside the nation.
  • Investment: — Expenditure, while increasing, is currently restricted in health-related AI and work is poorly funded and examined
  • Asymmetries and interpretations of data: — AI-based medical systems are most often faced with the challenge of asymmetric information between the physicians who use the technology and the programmers who designed the program. In fact, the understanding of AI innovations can be a primary cause of how successfully they can be used in care.

Therefore, design criteria are required to encourage the growth of reasonable AI. Key principles to encourage responsible AI include the following:

1. Transparency (user-visible operations)

2. Explainability (it is possible to trace the whole process followed by making a decision)

3. Suitability (comprehensibility)

4. Legitimacy (acceptable results should be there)

5. Auditability (it is simple to calculate efficiency)

6. Dependability (AI systems are operating as intended)

7. Recoverability (if necessary, manual control may be presumed)


Term “confidence”, sums up the greatest obstacle to the medical acceptance of AI. Patients don’t know whether they can trust new software to provide diagnoses, track their condition or interpret scans when nobody can explain in terms of how it works. A new Data Protection and Development Centre should be established to serve as administrator of data, including confidential information, to make it more accessible to entities within a set of standards and criteria so that the use of information is ethical.


Data protection standards allow entities (device and technology suppliers) to ensure that confidentiality is protected as a standard in any program and that it is configured to comply with the law. This is to prevent refurbishing measures of security into frameworks and code and to make privacy a prerequisite of layout instead of an afterthought.

Measures for physical and technical security should always be a fundamental principle of data privacy is to ensure that safety is sufficient to the quality of the data and the damage that could be incurred by misuse.


Artificial Intelligence has a variety of medical applications which can be carried out by analysing through descriptive, predictive and prescriptive artificial intelligence. AI-powered frameworks are followed by some challenges — they require an effective legal structure to regulate confidentiality and authentication while addressing issues of acceptability, medical intervention, transparency, and clarification. The steps needed to create a thriving health care environment for AI are-

  • Strong open data policy
  • Rigorous confidentiality regulation
  • Deploying labour forces with the relevant skills to embrace AI
  • Preparation for the improvements that AI will bring and a regulatory regime that maintains accountability and transparency but does not impede the advancement

Aarsh, Co- Founder & COO, Gravitas AI



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