AI in Healthcare

Artificial intelligence (AI) is a branch of computer technology that uses data-science and machine learning algorithms to perform tasks which typically require human intelligence. We already see extensive use of AI technologies, from voice assistant to driving a car and much more, not only in our daily life but also in the modern corporate world too. The healthcare industry is no exception to this.

Continuous research and development is resulting in increasingly sophisticated AI technology with incredible ability to replicate human capabilities with a higher efficiency and at a lower cost. Providing healthcare predominantly involves decision making based on data. AI, with its ability to analyse large datasets, aids healthcare professionals in those decision-making tasks with are usually done only using human intelligence. Therefore, it is no surprise that the use of AI is increasing in the healthcare eco-system and is transforming the way healthcare is provided.

Generally, in healthcare, AI is used to analyse and understand complex and voluminous data to identify patterns in order to generate meaningful predictions and approximate conclusions in both patient care and administrative processes. Although the use of AI in the healthcare industry is ever expanding, currently, its use can be classified in the following categories:

Prevention:

Prevention is better than cure’ is an popular age-old saying, but yet very relevant since the preventive mindset is crucial for our general well-being. AI plays an important role in helping people with proactive life-style management and to stay healthy.  Various technology driven applications and products gather real-time physiological data, process this data to infer meaningful conclusions, and thereby raise awareness about health status and the impact of certain lifestyle on health. They look for patterns, suggest lifestyle improvements and help people take control of their health and well-being. Such knowledge derived by processing voluminous data is also helping the healthcare professionals to understand the patterns better and accordingly provide better guidance and support to the patients.

Early detection:

Early detection is the key for efficient diagnosis and treatment of a medical condition. Although in case of some conditions, symptoms show up early enough to seek specialist help, there are many others which do not show any early warnings or those symptoms show up late which pose serious challenges to provide quality and effective treatments. AI is significantly helping in cases of such asymptomatic conditions by early detection based on symptoms that are too subtle for humans to detect, patient data and genetic history.  AI algorithms evaluate risks and trigger alerts at early stages making it possible to seek timely medical help. With the latest discoveries like implantable bio-compatible AI technology, it has become possible to gather and analyse the healthy and pathological patterns in biological signals such as heartbeats in real time without medical supervision. Such and other technological advancements are of immense help to the medical fraternity especially in the treatment of critical illnesses like the cardiovascular disorders, neurological disorders and cancers.

Diagnosis:

Sometimes correct diagnoses require study and interpretation of data relating to the disease like symptoms, case studies etc. While larger and better quality data certainly helps the physicians in correct diagnoses, it is challenging for them to manually collect and analyse such voluminous data. Therefore, AI is used not only to collect, store and retrieve large health data from different sources across the globe but also interpret the data, see the patterns and come to conclusions the way the professionals do it, that too much quicker resulting in more efficient and quality patient care.

Treatment:

AI and AI enabled Robotics and IoT are being extensively used to assist and enhance the expertise of the healthcare professionals in treatment of patients. Variety of clinical tasks and functions from routine and repetitive in nature to most complex surgical procedures are being automated with the help of technology. AI is extensively used in disease and patient management for formulating treatment plans and their compliance to such treatment plans especially in case of the long-term ones. AI enabled Robotics and IoT are also used in healthcare facilities like rehab centers, physical therapy and labs for many of the routine procedures and functions including sample collection and management, testing and analysis, providing nursing care to patients like lifting and moving them from the bed to the wheelchair, assist them in move etc.

Clinical decision making:

Accurate clinical decisions are crucial for quality patient care. Informed clinical decisions involve analysis and interpretation of voluminous patient data to understand the medical condition, possible treatment options and their outcomes. Advances in AI technology is extensively used to assist the professionals in collecting a large volume of patient data to reach the optimal clinical decisions. Use of technology in clinic decision making process can reduce the work-related stress of the clinicians and also enhance diagnostic accuracy and timeliness of decisions, thus becoming a possible solution to reduce diagnostic errors, one of the major challenges faced by the healthcare industry.

Drug development:

New drug development is a very long process, typically taking many years in most cases and costing huge sums of money. That’s because, to decide a possible therapy for a disease, scientists must evaluate numerous possible drugs that could be produced with different chemical combinations, analyse each one of them for their effectiveness or otherwise – all by following predominantly a manual trial and error method. Although AI is not yet ready to replace human intelligence in drug discovery any time soon, the pharma companies are beginning to use the AI to reduce the timeline at least at the initial screening stage of the drug development by eliminating some of the manual process employed during this stage. Such processes require processing large volume of data to recognize patterns and to make predictions to identify the promising therapies at the early stages and include:

• analyze and tailor chemical properties and create new and unique molecules from scratch faster.  
• explore real vast areas of chemical space and generate molecules that can be synthesised in the laboratory.
• help better predict if experimental drugs have any common and harmful side effects.
• analyse small and large datasets to explain the connections between molecular structure and bioactivity.
• analyse large volume of patient data to identify the impact of the drug on the disease helping researchers to focus on those compounds that are more effective.
• conduct a large volume of miniature experiments and predict whether experimental drugs have their desired effect without harming healthy cells.

Though the role of AI is generally limited to the initial phase of the drug discovery, analysis of huge data sets with the help of AI is helping the researchers to gain valuable insights which would not have been a time consuming and costly affair otherwise.

Patient Data:

Patient data, commonly called as electronic health records (EHRs), are the digital records of a patient’s medical history. They play an important role in clinical decisions, predictive tasks, pattern recognition, treatment etc. According to studies conducted, the doctors spend a lot of time reviewing the patient data resulting in burnouts and possible poor patient care. AI assisted applications are increasingly being used in:

• Clinical documentation to reduce manual administrative burden on clinicians.
• Automated patient data retrieval and analysis assisting the doctors in faster diagnosis and decision making.
• Uncovering patient insights, predict high-risk conditions to provide more effective and personalized care.
• Achieving interoperability in clinical documentation to enable analysing information across different EHR systems.

Despite advances in the technology, the use of Artificial Intelligence in healthcare is at its early stages with significant opportunities for expanding its usage within the healthcare eco system. With tech giants and healthcare providers committing more and more resources for automation in healthcare landscape, one of the questions always posed is whether Artificial Intelligence will eventually replace or reduce the need for human physicians, especially in the clinical setup. The answer to this, at least for now, seem to be that AI is more likely to be a tool to help the human physicians in clinical setup rather than a replacement. The biggest challenge, however, is not whether the technology is capable enough to be useful in healthcare, but it is our ability and willingness to adopt it in daily clinical practice. Perhaps only those who refuse to understand the full potential of the technology and work alongside will lose out and left behind in the long run.