Artificial intelligence (AI) is the talk of the town. Though the term AI and its uses are not new, however, in the past few years there has been a phenomenal growth in AI research and its applications. Today, AI is making its way in almost every sector. Feeding on a large amount of data, AI can mimic human-like tasks and activities to solve complex problems. Not only this, the AI is capable of learning, reasoning, and self-correcting to achieve the end result for a complex problem. Apart from other industries, AI has a vast potential in healthcare also. Today, AI is in use for some initial healthcare purposes such as detection of disease, drug development, delivery of health services, management of chronic disease, and many others. It has other potential applications also such as planning and resource allocation. In the coming years, AI is expected to disrupt the overall functioning of the healthcare industry by ensuring healthier and longer lives for human beings.
Applications of AI in healthcare
Healthcare is one of the front-runners in the adoption of technology and innovation. Over the past few decades, the healthcare industry has transformed significantly. In the coming years, it is expected to further develop with the adoption of AI-based healthcare applications. Some of the prominent applications of AI in healthcare include:
- Preliminary Diagnosis – The AI has capabilities to diagnose the diseases at a much higher rate than the healthcare professional. Apart from the diagnosis, it can significantly reduce the chances of medical error. The medical error or misdiagnosing cost a large number of lives every year. AI can address both issues. As of now, AI is in use for early cancer diagnosis, identifying brain tumors, predicting Alzheimer’s disease, and many others. In the future, the scope of AI use for diagnosis is expected to broaden for many other diseases with much higher accuracy.
- Providing personalized medicine – The AI can collect, store, and analyze large data sets at a much faster rate. Similarly, if some genetic variation arises in the data or a small set of populations shows some different characteristics from the groups of patients AI can further analyze it and can be helpful in developing targeted therapies.
- AI in healthcare cybersecurity – In today’s digital era cyber attacks are quite common. Among all the industries, healthcare is one of the most prone sectors to cyber attacks. In the past, it has been observed how personal and medical health data is easily compromised. The AI is capable of maintaining the data safe and secure.
- Predicting the risk of important outcomes – AI is also helpful in predicting certain health outcomes such as the risk of readmissions in patients with heart failure, mortality, and patient health outcomes during and after hospitalization, and many others. AI can analyze various electronic medical records data and past medical history to predict health outcomes.
- Drug Creation/clinical research – The other advanced application of AI is in drug development and biomedical research. The drug development process is a labor-intensive job that requires a very high amount of cost, time, and research. The deployment of AI can significantly increase the efficiency of the drug development process. In fact, as of today, worldwide many companies are utilizing AI-driven platforms for the drug development and research process. One of the notable companies is BioXcel Therapeutics. BioXcel Therapeutics, Inc. is a clinical-stage biopharmaceutical company utilizing the AI-based drug development process to identify the next wave of medicines across neuroscience and immuno-oncology.
Similarly, some of the other companies such as Standigm (Novel Drug Design), Genome Biologics (Preclinical Drug Discovery), Recursion Pharmaceuticals, DeepCure (Small Molecule Therapeutics), and many others are investing and utilizing AI for drug development.
Apart from these some other applications of AI in healthcare include Virtual/Personal health care assistants (such as chatbots), planning treatment design, healthcare administration and Workflow, X-ray readings/Screening, and many others.
Issues and Challenges
Every aspect has its pros and cons. With the tumult of advantages, the use of AI brings ethical, social, and technical issues with it. Some of the major concerns that can hinder the widespread adoption of AI in healthcare include:
- Data privacy and security – Data privacy and security is always a big challenge for internet-based services. Similarly in the case of AI-enabled healthcare services that collect, store, and analyze large amounts of data, need to be confident that their sensitive information is safe, secure, and well-protected.
- High costs of creation and maintenance – Initially the cost of implementing AI services is quite high and apart from this it requires regular up-gradation and maintenance cost.
- Lack of standard rule and regulation – Different countries worldwide follow different Data Protection Laws. There is a lack of uniformity in data handling and storage. Similarly, the law pertaining to fraud detection, privacy, a criminal investigation, and trade negotiations policies vary widely. However, a uniformed regulatory framework and policy can significantly address these legal issues associated with AI in healthcare.
Apart from these lack of personal involvement or human emotion (especially for heart surgeries), biasness of developer/system creators, data quality and consistency, unemployment, a direct impact on the autonomy and authority of the healthcare professionals are some of the other issues in AI healthcare.
Recent trends and developments in AI in healthcare-
The AI has a wide variety of applications and has the ability to provide affordable and accessible care to patients, simplify the healthcare administrative process (or workflow), and can also assist physicians in decision making. In order to fulfill the need and to take an early competitive advantage, worldwide, a large number of companies are working on the development of AI-based healthcare-based products and solutions. Among them, some of the leading companies include Caption Health, IBM Watson Health, Oncora Medical, Cloud MedX Health, Babylon Health, Corti, and many others.
Today, looking at the huge potential for AI in healthcare many AI-driven startups over the years have received significant global funding and many of them have entered into collaborations and partnerships with pharmaceutical companies and hospitals. Some of the prominent deals signed in recent years include Exscientia and Celgene’s (AI)-based drug discovery collaboration; BenevolentAI and Novartis’s collaboration (to find potential new uses for oncology drugs already in Novartis pipeline); Johns Hopkins Hospital and GE Healthcare’s collaboration to use predictive analytics and innovative problem-solving to improve the efficiency of patient operational flow; Iktos and Janssen collaboration to increase speed & efficiency of small molecule drug discovery; and many other.
As of now the use of AI is limited for pre-defined problems or to carry out some specific routine tasks. For instance, Today, Operational inefficiencies are one of the major issues for the healthcare industry. The AI has the potential to overcome challenges such as handling patient flow, hospital administration, designing treatment plans, and the optimal use of healthcare resources. Addressing all these critical issues can bring operational efficiency for healthcare organizations. Similarly, the AI has the capability to diagnose and predict the disease at a much higher rate than the professional. It saves a lot of time and effort put forward by healthcare professionals. Apart from these, AI has many other benefits and advantages such as a higher accuracy rate for clinical data interpretation, medical diagnosis, reducing the workload of nurses and medical support personnel, and many others.
Thus in the coming year, AI is expected to play a more decisive role in the healthcare industry. With the increasing accessibility and affordability of AI, it is expected to significantly improve the healthcare outcome for the patients.