AI in Healthcare: Challenges, Opportunities and Effectiveness

AI in Healthcare: Challenges, Opportunities and Effectiveness

In 2020, the series 'NEXT' premiered on FOX, portraying a rogue AI capable of self-improvement that poses a threat to humanity. While the show highlighted AI's potential impact on healthcare, real-world applications are far from ominous. In reality, AI is enhancing healthcare infrastructure.

Artificial Intelligence, or AI, has become an integral part of our lives. Statistics indicate that 35% of businesses have already adopted AI, and 77% of the devices we use incorporate artificial intelligence in their functionality. According to a PwC study, AI could contribute up to 15.7 trillion USD to the global economy by 2030. It is clear AI is disrupting every industry globally, including healthcare.

AI was first established in 1950. However, it did not enter in healthcare industry until the early 1970s with the development of MYCIN, an AI program that helped identify treatments for blood infections. Today, hospitals, health systems, and other healthcare organizations are keen to use generative AI. A wide array of projects and use cases are being implemented to see whether this new technology can provide a financial and clinical return on investment.

So, it is evident that we are entering a new era in medicine where AI may be used in clinical practice through risk assessment models, increasing workflow efficiency and diagnostic accuracy. This blog aims to inform you about the history, current state, effectiveness, and prospects of artificial intelligence in healthcare.


What is "Healthcare AI"?

 Healthcare AI refers to the use of artificial intelligence methods to evaluate medical data and improve several facets of healthcare. It includes several different technologies, including:

1) Machine Learning (ML): An approach to data analysis called machine learning that automates the process of creating analytical models. This area of AI is based on the notion that machines are capable of learning from data, identifying patterns, and making decisions with minimal help from humans. ML in healthcare enables artificial intelligence to examine patient data and medical records to identify diseases, suggest treatments, and identify potential health risks.

2) Natural Language Processing (NLP): A subfield of AI and computer science that deals with human language is Natural language processing. NLP enables computers to understand, generate, and manipulate human speech and text. In healthcare, NLP allows AI to analyze clinical notes, doctor-patient conversations, and medical literature to extract insights and automate tasks.

3) Deep Learning: A subset of ML that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain is called Deep learning. In healthcare, Deep learning excels at analyzing complex data like medical images (X-rays, MRIs) to identify abnormalities and support diagnoses.


History of AI in Healthcare:

The term 'artificial intelligence' was coined in a Dartmouth College conference proposal in 1955. Alan Turing, an English computer scientist, published Computer Machinery and Intelligence, which proposed the Turing Test, a theoretical way to differentiate between humans and AI by asking questions about whether a machine can think. Therefore, Turing is often called the "father of AI". However, AI applications did not enter the healthcare landscape until the early 1970s. In that era, MYCIN research produced an AI program that helped identify blood infection treatments. The proliferation of AI research continued, and in 1979 the American Association for Artificial Intelligence was formed which is reconstituted as the Association for the Advancement of Artificial Intelligence now.  


Throughout the 1980s and 1990s, the design of new AI systems helped achieve medical advancements such as:

1) Faster data collection and processing.

2) Assisting in more precise surgical procedures.

3) In-depth Design-by-Analogy (DBA) research and mapping.

4) More comprehensive implementation of electronic health records (EHR).


Effectiveness of AI:


1) Better accessibility: Even in 2024, many developing countries still have poor healthcare facilities. According to the World Health Organization (WHO), limited or zero healthcare accessibility is responsible for the 18.1-year gap in life expectancy currently recorded between the world’s richest and poorest nations. With AI intervention, these countries can finally have an efficient healthcare ecosystem as AI-backed digital systems can facilitate patient diagnosis and treatment.


2) Speedy Data Sharing: Manually maintaining medical records and data in a hospital is a hectic job. Artificial intelligence can be a great help here. Artificial intelligence in healthcare can locate particular patient data more effectively than conventional care, giving doctors more time to concentrate on medications and treatments. To fully realize the potential of AI and precision medicine, algorithms must be able to analyse large amounts of data quickly.


3) Early diagnosis: AI-driven healthcare technologies can use personal data to evaluate individuals' past and current health problems. Healthcare providers are better equipped to make accurate diagnoses by comparing the details of the diseases. Several mobile apps for healthcare have databases that have computed millions of diagnoses and symptoms. More significantly, it can predict the possible health problems a person may experience. For example, Google's Verily app is designed to predict hereditary and non-contagious genetic diseases.


4) Faster and cheaper: AI has revolutionized healthcare by speeding up patient examination and diagnosis, significantly cutting costs. For example, AI can quickly detect biomarkers indicating disease, reducing the need for manual analysis. This automation saves lives by enabling faster responses. AI algorithms are also cost-effective, reducing the number of lab visits needed by predicting outcomes based on patient data. This has led to a substantial increase in AI adoption across healthcare, with an 88% rise in organizations making the switch in the past year.


5) Improved Care For Patients: AI can improve patient experiences in healthcare by helping them navigate information quickly, access reports, and find their way in busy facilities, reducing confusion. A recent study found that 83% of patients are most bothered by poor communication in healthcare settings. Another significant benefit of AI for patients is its 24/7 availability. Babylon, for instance, is an app that enhances patient experiences by intelligently checking medication side effects.


6) Assistance in surgery: Artificial intelligence has made significant developments in robotics and machine learning for surgery. Dedicated AI Surgical Systems can perform the tiniest movements with 100% precision, enhancing the efficiency of complex operations while reducing risks such as side effects and blood loss. Patients benefit from faster recovery, aided by Antibacterial Nanorobots that eliminate infections before surgery. Real-time AI updates on patient conditions reassure patients and aid surgeons, particularly during procedures under general anaesthesia.


7) Robots offering support: Robots now assist patients alongside medical staff. Exoskeletons help paralyzed individuals regain mobility on their own. AI-powered prostheses with sensors offer more responsive limbs. Service robots handle daily tasks and provide companionship. Companion robots perform medical checks like monitoring sugar levels and blood pressure, and even assist with medication. Some robots are designed to support depressed patients by analyzing their moods and providing emotional support to improve their well-being.


A successful example of AI in healthcare:


AI is making significant strides in healthcare, as seen with China's Tsinghua University developing the AI-powered "Agent Hospital." Here, virtual doctors, nurses, and patients interact autonomously using large language models (LLM). AI doctors in this virtual hospital can treat 10,000 patients within just a few days. This task would take human doctors over two years. These AI doctors have achieved an impressive 93.06% accuracy in diagnosing major respiratory diseases, handling the entire patient care process from consultation to follow-up. On the other hand, healthcare facilities of the US, Japan, Denmark, etc are also using AI.


Here's how AI changing healthcare in India:

The field of artificial intelligence, or AI, is quickly changing the healthcare landscape in India. It has the potential to significantly improve patient outcomes, boost productivity, and extend accessibility. Here, AI is utilized for remote care, virtual assistance, medical history analysis, drug discovery and development, diagnostics and disease detection. Despite the challenges, the Government of India and the corporations are making significant investments in AI for healthcare. According to IndiaAI, Artificial Intelligence in the healthcare market in India is projected to grow at a CAGR of 40.5% from 2020 to 2025, with a market value of 1.6 billion USD. With further advancements in technology and addressing these obstacles, AI has the potential to transform healthcare delivery in India, making it more accessible, cost-effective, and efficient for all.


Challenges of AI implementation in healthcare:

  • Data Quality and Bias
  • Transparency and Explainability
  • Ethical Considerations



Despite all of the challenges, the potential of artificial intelligence in healthcare is undeniable. According to Tractica, The market for AI-powered healthcare is expected to grow to over 34 billion USD by 2025. However, it is important to remember that AI cannot replace doctors or other healthcare professionals. Why so? Because artificial intelligence lacks empathy, creativity, and ethical judgment. These are essential for medical professionals. To handle their patients' feelings, solve challenging issues creatively, and make morally and dignified decisions, they must be able to empathise. However, AI is a powerful tool that can augment caregivers' skills and knowledge. It can assist doctors in diagnosis, data analysis, and research. If researchers, doctors, and legislators work together, then they can harness AI's potential to build a future where everyone has access to better healthcare