It is widely accepted that AI technologies will support and improve human labour rather than completely replace that of doctors and other healthcare professionals. AI is prepared to assist healthcare workers in a range of duties, including clinical documentation, patient outreach, administrative processing, and specialist help in areas like image analysis, patient monitoring, and medical device automation.

1. Disease Diagnosis Applications: This includes medical imaging, pathology, disease risk prediction, drug discovery remote monitoring and telehealth, early warning systems etc…Artificial intelligence uses CT scans, electrocardiograms (ECG), cardiac MRI images, skin images, retinal scans, and X-Ray scans to detect cancer, stroke, diabetes, and other diseases. AI algorithms make use of large volumes of high-quality healthcare data to classify or predict diseases with comparable or even better accuracy than human experts
2. Treatment Applications: Includes Personalized treatment plans, drug discovery and development, real time decision support, predictive analysis, rehabilitation and physical therapy, surgical assistance etc… AI-driven treatment applications in healthcare have the potential to improve patient outcomes, reduce costs, and enhance the overall quality of care. However, ethical considerations, regulatory compliance, and the need for collaboration between AI and healthcare professionals are essential for the safe and effective implementation of these technologies.
3. Drug Discovery Applications: AI-driven treatment applications in healthcare have the potential to improve patient outcomes, reduce costs, and enhance the overall quality of care. However, ethical considerations, regulatory compliance, and the need for collaboration between AI and healthcare professionals are essential for the safe and effective implementation of these technologies
4. Personalized Medicine Applications: Personalized medicine has the potential to revolutionize healthcare by providing more effective and efficient treatments while reducing adverse reactions and unnecessary treatments. It emphasizes the importance of an individual’s unique genetic and clinical profile in healthcare decision-making. However, implementing personalized medicine involves addressing ethical, privacy, and data-sharing considerations, as well as the need for specialized training for healthcare professionals and integration into clinical practice
5. Precision medicine: Precision medicine is an emerging approach to healthcare that aims to tailor treatments and interventions to the individual characteristics of each patient, such as their genetic profile, lifestyle, and environmental factors. AI is used to produce personalized treatment plans for patients that take into account such factors as their medical history, environmental factors, lifestyles, and genetic makeup.

Precision medicine projects come in a variety of forms, however they may generally be categorized into three kinds of clinical areas:

• Complex algorithms: Large datasets, including genetic, demographic, and electronic health record data, are fed into machine learning algorithms to predict prognosis and choose the best course of therapy.
• Digital health applications: Health monitoring data from wearables, mobile sensors, and other sources, as well as data entered by patients on their food intake, exercise, and emotional state, are all recorded and processed by healthcare apps. Several of these applications are classified as precision medicine apps because they employ machine learning algorithms to identify patterns in the data, improve forecasts, and provide tailored treatment recommendations.
• Omics-based tests: Machine learning algorithms are combined with genetic data from a population pool to identify patterns and forecast a patient’s reaction to therapy. To enable individualized therapies, machine learning is used with various indicators, such as protein expression, gut microbiota, and metabolic profile, in addition to genetic data.

Some of the possible applications of AI in precision medicine include:

• Identifying biomarkers and subtypes of diseases that can guide diagnosis and prognosis
• Developing personalized risk scores and prevention strategies based on genetic and environmental factors
• Recommending optimal therapies and dosages based on patient-specific characteristics and preferences
• Monitoring treatment response and outcomes using wearable devices and sensors
• Speeding up image acquisition in MR
• Discovering new drugs and targets for precision therapies using computational methods
• Helping radiologists read images faster and more accurately
• Taking the complexity out of ultrasound measurements
• Taking the complexity out of ultrasound measurements
• Augmented and virtual reality (AR and VR) can be incorporated at every stage of a healthcare system

We can categorize AI in healthcare into three groups:

• Patient Oriented AI
• Clinical Oriented AI
• Administrative and operational oriented AI

Below some of the applications of AI:

• Early and easy detection of ailments
• Treatment design and personalized medicine
• Managing medical records and other data
• Discover new drugs and therapies.
• Checking health through the wearable.

Role of Ai  in healthcare

Artificial intelligence (AI) is transforming the field of healthcare by providing innovative solutions for diagnosis, treatment, prevention, and management of various health conditions. It has the potential to revolutionize health care field by improving patient comfort, reducing costs and improving efficiency. AI helps in improving the quality, efficiency, and accessibility of healthcare services, as well as reduce costs and errors. It helps to analyze relationships between clinical data and patient outcomes, can develop person.

Below the role of AI in a nutshell

• Improve decision making. Machine learning systems that can learn from large amounts of data and provide insights, predictions, and recommendations for clinical decision making
• Perform Robot assisted surgery that can perform surgical procedures, assist in rehabilitation, and provide social and emotional support for patients.
• Analyse X- ray, CT and MRI scans, to diagnose medical conditions
• Associated Care: Associate care AI is a term that refers to the use of artificial intelligence (AI) to assist, augment, or automate the tasks of healthcare professionals, such as doctors, nurses, pharmacists, and therapists
• Expanded access to medical services – Expanded access to medical services is a crucial goal for improving the health and well-being of people around the world. Many countries face challenges in providing adequate and affordable health care to their populations, especially in rural and remote areas.
• Wearable devices and sensors that can monitor vital signs, track activity, and alert users and caregivers of potential health issues.
• Natural language processing systems that can extract information from clinical notes and records, generate summaries and reports, and facilitate communication between patients and providers 

BUILDING EFFECTIVE AND TRUSTED AI AUGMENTED HEALTH CARE SYSTEMS

Many of the AI products for healthcare is in the initial stage of building. After designing and developing the solutions for a problem and then stake holder is engaged. Stakeholders create a multidisciplinary team, and the team brings technical, strategic, operational expertise to define problems, goals, success metrics and intermediate milestones AI must be designed and deployed in a way that respects the values, needs, and expectations of patients, health care professionals, and society at large.

Some of the key challenges include ensuring the quality, safety, and reliability of AI systems; protecting the privacy and security of health data; promoting the transparency, explainability, and accountability of AI decisions; and fostering the human-AI collaboration and communication in health care settings.

FUTURE OF AI IN HEALTHCARE

Having a right platform and infrastructures will go a long way towards promoting wider adoption of AI in healthcare. There is more research needed to bridge the gap between existing model and the virtual model. We need large amount of high-quality trusted data.

Current AI system cannot reason the same way as humans. Long term AI will be becoming more intelligent with improved patient outcomes in a more cost effective way.AI chatbots and Ada are some examples.

To implement the use of artificial intelligence in health, healthcare leaders must take these issues into account as a minimum:

Processes for ethical and responsible access to data: Obtaining domain expertise or prior knowledge to make sense of and create some of the rules which need to be applied to the datasets (to generate the necessary insight) Healthcare data is extremely sensitive, inconsistent, siloed, and not optimized for the purposes of machine learning development, evaluation, implementation, and adoption.

Access to prior knowledge or domain expertise to help make sense of and develop some of the rules that must be applied to the datasets.The ability to access enough processing power to make decisions instantly, which is changing dramatically with the introduction of cloud computing.