Opinion Technology

AI in Healthcare: A comprehensive review of benefits and risks


It is no secret that artificial intelligence (AI) is rapidly evolving and growing more sophisticated every day. However, it can be difficult to understand how this technology can be applied in practical terms, especially for businesses.

In this blog post, we will explore AI in business through the lens of healthcare.

AI in healthcare

As artificial intelligence (AI) and related technologies continue to become more prevalent in our businesses and our lives, many are starting to explore how these powerful tools can be applied to the complex field of healthcare.

AI has already shown tremendous promise in helping to diagnose diseases, predict outcomes, and guide important research efforts in the medical field. For example, algorithms have been shown to outperform radiologists when it comes to detecting malignant tumours, while also helping researchers better understand which clinical trials are most likely to be successful.

While there is no doubt that AI has great potential in healthcare, there are a number of important factors that will need to be considered before it is widely adopted by providers, payers, or pharmaceutical companies.

AI Technologies used in healthcare

Artificial intelligence is a rapidly evolving field that holds great promise for improving healthcare and modern medicine. There are many different AI technologies that play an important role in healthcare, each with its own unique set of capabilities and applications.

Machine Learning:

One of the most widely used AI technologies in healthcare is machine learning, which involves using algorithms to process large amounts of data and identify patterns or trends. This technology can be used to analyse medical images, patient records, and other health-related data in order to make more accurate predictions about things like diagnoses, drug efficacy, and treatment outcomes.

Natural Language Processing:

Another powerful AI technology for healthcare is natural language processing, which enables computers to understand text-based information by analysing the meaning behind words and phrases. This technology can be used to support clinical decision-making by helping clinicians quickly access relevant research findings or clinical guidelines when making treatment decisions.

Rule-based systems:

Rule-based expert systems offer an intuitive and easily understandable approach to knowledge representation that is well suited to certain domains, such as healthcare. In healthcare settings, these systems are widely used for clinical decision support, helping clinicians make diagnoses and treatment recommendations based on the vast amount of medical data available today.

Robotic Process Automation:

Robotic process automation is a powerful technology that has great potential to revolutionise the way we approach healthcare. It uses sophisticated computer programs to perform structured digital tasks, much like a human user following a script or set of rules. This makes it an extremely effective tool for automating repetitive administrative processes, such as prior authorisation or updating patient records.

AI Use cases in healthcare

#1: AI for Diagnosis

One of the most promising applications of AI in healthcare is in the area of diagnosis. AI can be used to help doctors and other healthcare professionals more accurately diagnose diseases and conditions.

For example, we can use a machine learning algorithm to analyse CT scans from patients with cancerous tumours in the liver. This algorithm can detect signs of cancer in these scans with a very high degree of accuracy.

#2: AI for Treatment planning

AI has the potential to revolutionise the healthcare industry by helping healthcare providers develop more effective treatment plans for their patients.

One example of an AI technology that is being used in healthcare treatment planning is deep learning algorithms, which are able to analyse large amounts of data and make accurate predictions based on patterns in the data. By using deep learning algorithms, healthcare providers can quickly and accurately identify which treatments are most likely to be effective for a given patient, resulting in more efficient treatment planning processes.

#3: AI for Drug Development

One potential application of AI in the field of drug development is the use of machine learning algorithms to identify which compounds are most likely to be effective against a particular disease or condition. For example, we can use algorithms that can analyse large datasets of chemical compounds and predict how effective they may be at treating certain diseases, such as cancer. This technology has the potential to significantly streamline the process of discovering new drugs, potentially reducing costs and timeframes for drug development.

#4: AI for Patient Monitoring

AI has emerged as a powerful tool for improving patient monitoring and helping doctors and caregivers to take action when needed. One example of how AI is being used in this area is through the collection of data from wearable devices and other sources, which can be used by AI algorithms to identify changes in a patient’s condition that may indicate the need for intervention. This information can then be used to alert doctors or other caregivers, allowing them to take action quickly and improve the patient’s health.

#5: AI for Healthcare Administration

AI has the potential to help improve healthcare administration by making it easier and more efficient to process and organise electronic health records (EHRs). One example of how AI is being used in healthcare administration is through natural language processing, which enables computers to understand, analyse, and generate human language. This has been shown to be particularly useful for organising EHRs and making them easier to access and use.

Ethical concerns and Data privacy issues

AI has the potential to dramatically improve healthcare by providing faster, more accurate diagnoses and helping medical professionals make better decisions. However, there are a number of ethical concerns and data privacy issues associated with the use of AI in healthcare.

Ethical concerns:

One major ethical concern is that AI may be used to make or support medical decisions in ways that are not transparent or accountable. For example, a deep learning algorithm might be used to analyze an image from a CT scan and provide a diagnosis without any explanation as to how it came to that conclusion. This makes it difficult for patients to understand and trust their care, and could potentially put them at risk if they do not receive the appropriate treatment.

Data privacy issues:

Another issue is data privacy, particularly as it relates to the huge volumes of personal health information generated by AI tools. For example, patients may be hesitant to provide their health records and other sensitive information to an AI system if they are concerned about its security or how it will be used. Ensuring that data is kept private and secure will therefore be essential if we want people to embrace these new technologies.

Overall, while there are many benefits associated with the use of AI in healthcare, care must be taken to ensure that these technologies are implemented in ways that uphold ethical standards and respect patient privacy. If we can achieve this balance, we have the potential to greatly improve health outcomes for everyone around the world.


Given the tremendous potential of artificial intelligence in healthcare, it is clear that this field will play a critical role in our future offerings. Whether through machine learning and the development of precision medicine or with the use of imaging analysis and speech recognition, AI has the power to transform how we diagnose and treat patients.

While there are still challenges to overcome, I am confident that we will see great advances in this area as scientists continue to unlock the full potential of this technology.

For the benefit of patients around the world, it is essential that we embrace AI and leverage it to improve healthcare outcomes for all.


Aruna Pattam is a Global AI thought leader and is currently the head of AI, Analytics & Data Science.

She has won awards recently including “The Most Admired Global Indians 2021” by Passion Vista, “AI Changemaker Leader 2022″ by 3AI, and “AI Global Ambassadar 2022” by Swiss Cognitive AI Community.

Aruna has spent the last 22+ years delivering analytics platforms, decision support systems and analytics solutions using analytics, artificial intelligence, and machine learning.


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