AI In Healthcare: Transforming The Future Of Medicine

From robot-assisted surgeries to AI-driven diagnostics, the technology is transforming global medicine, making care more precise, accessible, and cost-effective. Artificial Intelligence (AI) is revolutionising healthcare with companies like Tempus, Aidoc, and Exscientia driving innovation, while the healthcare AI market is projected to reach $187.95 billion by 2030.

Artificial Intelligence (AI) is transforming the healthcare industry by enabling smarter and faster decision-making. It refers to the use of algorithms and machine learning systems to process complex medical data, helping doctors and healthcare providers improve diagnosis, treatment, and patient outcomes.

"We need to design and build AI that helps healthcare professionals be better at what they do. The aim should be enabling humans to become better learners and decision-makers,” says Mihaela van der Schaar, PhD, University of Cambridge.

Adopting AI in healthcare could reduce costs by 5% to 10%. The healthcare AI market is projected to grow to $187.95 billion by 2030. Additionally, by 2026, the market for robot-assisted surgeries could reach $40 billion.

AI-powered tools can detect diseases like cancer at an early stage by analysing X-rays or MRIs, reducing errors and improving patient survival rates. AI can analyse large datasets, such as medical records or imaging scans, far more quickly and accurately than human experts, making it an essential tool in modern medicine.

Google’s DeepMind has developed AI systems to detect diabetic retinopathy and predict the structure of proteins, helping to understand diseases at a molecular level.

Viz.ai introduced AI tools for stroke detection, helping care teams respond faster to emergencies. Recursion Pharmaceuticals utilised AI in drug discovery, accelerating the development of new treatments by analysing biological datasets. 

Companies like PathAI are improving diagnostic accuracy in pathology, while Tempus uses AI to personalise cancer treatments by analysing patient-specific data. Aidoc develops AI solutions that assist radiologists by flagging urgent abnormalities in medical images, aiding in the prompt diagnosis of conditions like strokes and pulmonary embolisms.

The Evolution of AI in Healthcare

AI's involvement in healthcare started in the 1960s and 1970s with expert systems like MYCIN, which was designed to assist doctors in diagnosing infectious diseases and recommending antibiotics. Although innovative for its time, early systems faced limitations in their ability to integrate with clinical workflows and manage complex medical data.

In the 1980s and 1990s, the introduction of microcomputers allowed AI to expand its capabilities. Researchers explored the use of Bayesian networks, fuzzy logic, and neural networks to improve decision-making in medicine. However, practical applications remained limited due to a lack of computing power and data accessibility.

Key milestones in AI adoption in medicine

2000s – The Growth of Data
The digitisation of medical records, with the rise of Electronic Health Records (EHRs), provided AI systems with access to vast amounts of healthcare data. This led to the development of algorithms for predicting patient outcomes and analysing trends.

2011 – IBM Watson Health
IBM launched Watson Health, an AI platform using natural language processing to assist doctors in diagnosing complex conditions. This marked a shift towards AI systems becoming practical tools for clinicians.

2018 – FDA Approval of AI Diagnostics
The US Food and Drug Administration (FDA) approved the first AI-powered device for detecting diabetic retinopathy. This milestone demonstrated AI's ability to perform medical diagnostics autonomously, helping improve patient care.

2020s – Integration of AI in Everyday Healthcare
In the 2020s, AI became a crucial part of daily healthcare practices, with numerous companies contributing to its rapid integration. Freenome developed AI-driven tools for early cancer detection using blood samples, while Butterfly Network created handheld ultrasound devices powered by AI for quick and portable diagnostics.

Post-COVID-19 Era
The COVID-19 pandemic accelerated the adoption of AI, with systems being used to track the virus's spread, develop vaccines, and improve hospital management. AI has become an essential tool in responding to global health crises.

Frank Liao, PhD, UW Health, Wisconsin said "Since the start of the COVID pandemic, the number of incoming electronic messages to our providers has increased by 57%. [So we’re piloting] a generative AI tool that turns incoming messages into editable drafts. This helps providers save time while also getting them past blank screen syndrome."

Applications of AI in Healthcare

AI is rapidly transforming healthcare, providing innovations that improve patient care, enhance diagnostic accuracy, streamline administrative processes, and optimise treatment plans. Here are some of the key applications of AI in healthcare:

Medical imaging and diagnostics

AI-powered tools can analyse medical images such as X-rays, MRIs, and CT scans to detect diseases and conditions like cancer, heart disease, and fractures. Algorithms trained on large datasets of images can recognise patterns and anomalies that may be difficult for human doctors to spot.

AI systems like Google's DeepMind have shown the ability to diagnose eye diseases by analysing retinal scans and predict the onset of conditions like diabetic retinopathy with accuracy comparable to expert ophthalmologists.

Predictive analytics for early detection

AI has the ability to forecast the likelihood of diseases by examining a patient’s medical history, genetic profile, and lifestyle habits. Predictive algorithms identify individuals who are at elevated risk for conditions such as diabetes, cardiovascular diseases, or specific cancers, enabling timely preventive care and treatment.

Mayo Clinic uses AI-powered predictive analytics tools to assess the risk of cardiovascular diseases by analysing patient data, such as cholesterol levels, blood pressure, and medical history. These tools assist doctors in recommending preventive measures and tailoring treatment plans to individual patients.

Personalised medicine and treatment plans

AI can help create personalised treatment plans tailored to a patient's genetic makeup, lifestyle, and other factors. By analysing data from electronic health records (EHRs), AI can identify which treatments or medications are likely to work best for an individual.

AI is used in oncology to determine the most effective chemotherapy regimen for a cancer patient based on their genetic profile and the genetic characteristics of their tumour.

Virtual health assistants

AI-powered virtual assistants, such as chatbots or voice assistants, can provide patients with information about their health, remind them to take medication and answer basic medical queries. These assistants can help manage chronic conditions, schedule appointments, and assist with patient education.

Virtual assistants like Babylon Health use AI to diagnose symptoms, suggest treatment options, and provide virtual consultations, often working alongside human healthcare professionals.

Robotic Surgery

AI-driven robotic systems can assist in surgical procedures by providing more precision, improving outcomes, and reducing recovery times. These systems can be controlled by surgeons or function autonomously for certain types of procedures.

Intuitive Surgical's da Vinci system uses robotic arms, guided by AI, to perform minimally invasive surgeries with high precision, reducing the risk of complications.

Drug discovery and development

AI accelerates the process of drug discovery by analysing vast amounts of biomedical data to identify promising drug candidates. It can also predict how different compounds might interact with specific diseases and identify potential side effects, dramatically reducing development time.

AI-powered platforms like Atomwise use machine learning to screen millions of compounds for their potential to treat diseases, helping to identify new drug candidates faster and at lower cost.

Clinical trial optimisation

AI can optimise the clinical trial design by identifying suitable candidates, predicting patient responses, and analysing trial data in real time. AI also helps in managing the large amounts of data generated during clinical trials, making the process more efficient.

AI systems can analyse medical records to find patients who meet specific inclusion criteria for trials, improving recruitment and minimising human error in the selection process.

Natural Language Processing (NLP) for EHRs

AI uses NLP to analyse unstructured data in electronic health records (EHRs), such as doctor's notes, patient histories, and medical literature. This can improve patient care by extracting useful information for decision-making and providing more accurate diagnoses.

NLP tools can scan EHRs to identify trends in patient symptoms, medications, and outcomes, helping physicians provide more accurate, data-driven care.

Remote monitoring and telemedicine

AI can be integrated into wearable devices and telemedicine platforms to monitor a patient’s vital signs, chronic conditions, and post-surgical recovery. This allows for continuous patient monitoring and real-time feedback without needing frequent in-person visits.

AI in wearable devices like Fitbit or Apple Watch can track heart rate, blood oxygen levels, and other metrics to alert users and healthcare providers to potential health issues.

Healthcare operations optimisation

AI can streamline administrative tasks such as billing, scheduling, and claims processing. This can reduce costs, improve efficiency, and free up healthcare professionals to focus on patient care rather than administrative work.

AI-powered systems like Olive automates repetitive tasks in healthcare organisations, such as claims processing and appointment scheduling, reducing administrative burden and improving operational efficiency.

AI in Mental Health

AI tools are increasingly used for mental health diagnosis and support, from chatbots offering mental health counselling to predictive models that detect early signs of mental health conditions like depression and anxiety.

is an AI-powered chatbot that provides cognitive behavioural therapy (CBT) to users, helping to alleviate symptoms of depression and anxiety through interactive conversations.

AI for Infection Control and Monitoring

AI can be used to predict and track infectious disease outbreaks, helping to control the spread of epidemics or pandemics. It analyses health data, public health reports, and social media posts to provide early warnings and suggest containment strategies.

AI systems like BlueDot analyse data to predict and monitor the spread of infectious diseases, including the early detection of the COVID-19 outbreak.

Case Studies AI in Healthcare 

AI for Early Cancer Detection: Freenome
Freenome, a biotechnology company, uses AI to detect cancer at an early stage through blood tests. Their system analyses patterns in a patient’s blood to identify potential cancer indicators. This approach allows doctors to diagnose cancer earlier, increasing the chances of successful treatment.

Outcome:
Freenome’s AI-powered tests have shown promise in improving early detection rates for colorectal cancer, potentially saving lives by identifying the disease before symptoms appear.

AI in Radiology: Zebra Medical Vision
Zebra Medical Vision, an Israel-based company, uses AI algorithms to analyse medical imaging data such as X-rays, CT scans, and MRIs. Their AI platform is designed to detect a wide range of conditions, including osteoporosis, liver disease, and cardiovascular issues. By providing radiologists with detailed insights, Zebra helps improve diagnostic accuracy and reduce workloads.

Outcome:
Zebra Medical Vision's tools have been adopted by healthcare systems in multiple countries. Their AI has significantly enhanced early disease detection and improved radiology workflows, enabling radiologists to focus on complex cases while maintaining high diagnostic standards.

Personalised Treatment Plans: Tempus
GNS Healthcare employs AI-driven models to develop personalised treatment plans for patients with complex conditions, such as cancer and autoimmune diseases. By analysing vast datasets, including genetic information, clinical trials, and real-world evidence, GNS identifies the most effective therapies for individual patients.

Outcome:
GNS Healthcare has collaborated with hospitals and pharmaceutical companies to optimise treatment strategies. Their approach has enabled more precise therapies, reduced unnecessary treatments, and improved patient recovery rates, particularly in oncology and chronic disease management.

Drug Discovery: Exscientia
Exscientia, a UK-based company, uses AI to speed up drug discovery. Their platform analyses vast datasets to identify potential drug candidates. This reduces the time and cost associated with traditional drug development.

Outcome:
Exscientia has successfully developed drug candidates for diseases such as cancer and immunological disorders, bringing them to clinical trials much faster than traditional methods.

Remote Patient Monitoring: Biofourmis
Biofourmis uses AI and wearable devices to monitor patients’ vital signs in real time. Their platform predicts potential health issues before they become critical, allowing doctors to intervene early.

Outcome:
Hospitals using Biofourmis’ technology have reported fewer readmissions and better patient management, especially for those with chronic illnesses.

Mental Health Support: Woebot Health
Woebot Health offers an AI-powered chatbot that provides mental health support using cognitive behavioural therapy (CBT). The chatbot engages in conversations with users, helping them manage symptoms of anxiety and depression.

Outcome:
Woebot has made mental health support more accessible, particularly for individuals who may not have access to traditional therapy.

The Use of AI by NHS England

The National Health Service (NHS) in England has adopted AI technology to improve patient care and streamline operations. One of the key innovations is the use of AI in diagnostic tools, which helps doctors identify conditions faster and more accurately.

In 2021, NHS England began testing AI software developed by Google Health to assist doctors in detecting breast cancer from mammograms. The AI system was able to identify cancerous tissue with a higher level of accuracy than human doctors in some cases.

Outcome: This application of AI not only reduces diagnostic errors but also helps doctors prioritise urgent cases, improving overall healthcare delivery.

Challenges and ethical considerations in AI in Healthcare

AI relies on vast amounts of patient data, including medical records and genetic information. Ensuring this data is stored securely and used responsibly is a significant challenge. Breaches in data privacy can harm patients and erode trust in AI systems. Strict regulations like GDPR in Europe aim to protect patient information, but implementing these safeguards across all regions is complex.

AI systems can sometimes reflect biases present in the data used to train them. For example, an algorithm trained on data from a specific population may not work accurately for people from other groups. This can lead to unequal treatment and misdiagnoses. Addressing bias requires diverse datasets and rigorous testing.

Developing and deploying AI tools in healthcare requires approval from regulatory bodies to ensure safety and effectiveness. The process can be lengthy and varies across countries. AI developers must meet strict standards, which can slow down innovation and adoption.

AI's role in decision-making raises ethical questions. For instance, should an AI system’s recommendation always take precedence, or should doctors override it? There are also concerns about the potential for AI to reduce the human element in healthcare, which is critical for patient trust and care.

Addressing these challenges requires collaboration between AI developers, healthcare providers, policymakers, and ethicists to ensure AI is used responsibly and equitably.

Future Prospects of AI in Healthcare

The future of artificial intelligence (AI) in healthcare looks promising, with significant advancements expected in the coming years. Here are some key areas where AI is set to make an impact:

AI and Robotics in Surgeries
AI-powered robots will assist doctors in performing complex surgeries with greater precision and minimal risks. These robots can help in tasks like organ transplants and brain surgeries. In the future, remote surgeries using AI and robotics may become common, allowing specialists to operate on patients anywhere in the world.

Improving Healthcare in Developing Countries
AI has the potential to bring affordable healthcare to remote and underserved areas. Tools powered by AI can diagnose diseases such as malaria or tuberculosis using mobile apps or basic equipment. This could save lives in regions with limited access to doctors and advanced facilities.

Collaborations Between Tech and Healthcare Companies
Technology companies and healthcare providers are expected to work together more closely. These collaborations can lead to innovative tools for early disease detection, new medicines, and better patient care systems. Such partnerships will help healthcare providers use AI effectively.

Reducing Inequalities in Healthcare
AI can help reduce healthcare gaps by identifying communities that lack proper medical services. Predictive tools can analyse data to suggest where resources like vaccines, clinics, or doctors are needed the most, helping to improve healthcare access for everyone.

Smart Hospitals with AI, IoT, and 5G
AI, when combined with the Internet of Things (IoT) and 5G networks, can create smart hospitals. IoT devices can monitor patients in real-time, while AI processes the data to predict potential problems. With faster internet speeds, doctors can receive updates instantly, leading to better and faster treatments.

Predictions for Global Healthcare Systems
By 2030, AI is expected to be a vital part of healthcare systems worldwide. AI tools will help doctors personalise treatments, predict outbreaks, and improve overall efficiency. It will also play a key role in managing global health crises, ensuring quicker responses to pandemics.

AI in healthcare promises to improve the quality of care, make treatments more accessible and create a more efficient healthcare system for all. However, to achieve this, challenges like data privacy and ethical concerns need to be carefully addressed.

Shikha Negi

Shikha Negi is a Content Writer at ztudium with expertise in writing and proofreading content. Having created more than 500 articles encompassing a diverse range of educational topics, from breaking news to in-depth analysis and long-form content, Shikha has a deep understanding of emerging trends in business, technology (including AI, blockchain, and the metaverse), and societal shifts, As the author at Sarvgyan News, Shikha has demonstrated expertise in crafting engaging and informative content tailored for various audiences, including students, educators, and professionals.