AI in healthcare industry

AI in Healthcare: How Is It Revolutionizing This Critical Industry?

AI in healthcare is maximizing the potential of this industry to save lives in unthinkable ways, from drug research and development to precision medicine, clinical documentation, virtual assistants, and more. This blog post will provide you with insight into the role of Artificial IntelligenceI in healthcare, along with its types, benefits, challenges, and more.

With the introduction of AI in healthcare, we are seeing a gradual shift in diagnosis, treatment, administrative tasks, and more.

Unlike old times when doctors used to prescribe with the idea that this is what they usually prescribe to patients with similar symptoms, the introduction of artificial intelligence in healthcare has facilitated the shift towards prescribing personalized medicine based on the patient’s unique characteristics, including genetics, medical records, lifestyle factors, and previous treatment responses. This shift effectively eliminates the one-size-fits-all approaches toward treatments.

This is just one of the simplest examples rather than uses of Artificial Intelligence in healthcare. There is more to it, which we will learn in this blog.

What is AI in Healthcare?

AI in healthcare refers to the use of artificial intelligence (AI) technology and several other cognitive technologies like natural language processing and machine learning to develop AI-powered software solutions or computer systems to be used in clinical facilities. It also involves the use of various techniques like predictive analytics, pattern recognition, probabilistic inference, deep learning, and more.

These software solutions can enhance various aspects of the medical field with real-time insights, accuracy, personalization, consistent monitoring, and more to deliver more effective patient care.

AI in Healthcare Statistics

To have a glimpse into how much gravity the future of healthcare AI holds, let us have a look at the statistics showing the current and future projections.

  • With the reduction of 86% of errors made by healthcare workers through the use of AI in healthcare, it could save more than 250,000 lives each year.
  • The market is projected to reach USD 95.65 Billion by 2028, up from USD 6.60 Billion in 2021, at a compound annual growth rate (CAGR) of 46.1%.
  • The pharmaceutical & biotechnology startups segment is projected to grow at a high-paced CAGR from 2022 to 2028.
  • As per Statista’s report, the global AI digital health market by major segment for selected years between 2015 and 2025. It is estimated that it will reach nearly 190 billion U.S. dollars by 2025.
  • Having seen the transformation AI has welcomed in the healthcare industry, it is projected that AI applications can cut annual US healthcare costs by USD 150 billion in 2026.
  • According to Allied Market Research, the global healthcare chatbots market reached $116.9 million in 2018 and is projected to reach $345.3 million by 2026, with a compound annual growth rate (CAGR) of 14.5% from 2019 to 2026.

What Are The Types of AI in Healthcare?

Let’s learn about the types of artificial intelligence in healthcare here in detail:

Natural Language Processing(NLP)

In the healthcare sector, NLP, a subset of AI, facilitates the analysis, comprehension, and manipulation of human-generated healthcare data such as medical records, clinical notes, research papers, and patient interactions. 

The major purpose of employing NLP in clinical processes is to extract valuable insights (clinically relevant information) and automate tasks. This involves tasks like creating, comprehending, and organizing documents using techniques like text categorization and topic modeling. 

NLP serves as an analytical tool for unstructured patient data, automatically generating reports on patient diagnoses and transcribing patient communications through speech recognition or optical character recognition (OCR) technology. 

Here is a list of some of the applications of NLP in healthcare:

  • Electronic health record (EHR) abstraction
  • Voice-to-text transcription for medical notes
  • Clinical documentation improvement
  • Automated coding and billing
  • Clinical decision support systems
  • Virtual health assistants and chatbots

Machine Learning(ML)

When it comes to healthcare and machine learning, it involves using algorithms and statistical models on high-quality structured data. Analyzing this data provides accurate health-related predictions based on the valuable insights it has found from the data to assist doctors in effective decision-making.

The aim of developing machine learning-powered solutions for the healthcare sector is to improve patient care, diagnosis, treatment, and overall healthcare management.

The top applications of ML in healthcare are:

  • Precision medicine
  • Drug discovery and development
  • Medical imaging analysis
  • Genomic sequencing and analysis
  • Drug adverse event detection
  • Healthcare fraud detection

Now, let’s learn about two major aspects of ML: 

Artificial Neural Networks(ANN)

Inspired by the structure and function of the human brain, an Artificial Neural Network (ANN) is a fundamental component of many machine-learning and deep-learning (which we will learn next) algorithms. It comprises interconnected nodes, called neurons, organized into layers.

ANNs are capable of learning complex patterns and relationships from data and are widely used in healthcare for various tasks, including medical image analysis, disease prediction, drug discovery, and clinical decision support.

Deep Learning

Deep Learning, a subset of machine learning, has gained significant attention in healthcare due to its ability to handle large, complex datasets and extract intricate patterns. 

It leverages the capabilities of neural networks to acquire knowledge from data, recognize patterns, and make predictions or decisions, leading to advancements in diagnosis, treatment, drug discovery, and patient care. To do so, techniques like supervised, unsupervised, and semi-supervised learning are deployed.

For health startups or organizations venturing into leveraging the latest technologies like IA/ML, accessing skilled machine learning developers to bring their development ideas to life isn’t always easy. At MindInventory, we understand the challenges of finding the right talent for your machine-learning projects. That’s why we offer a dedicated team of experienced ML developers for hire who are committed to driving innovation in the healthcare industry. 


Patient care, medicine, and other facets are crucial aspects of the medical sector. Another vital aspect is administration, which fuels the smooth functioning of this sector.

However, manual record-keeping, extensive laboratory tests, manual patient record management, on-paper appointment scheduling, and more have left room for inefficiencies, errors, time consumption, and cost consumption.

These tasks are also significant contributors to healthcare worker burnout. Automation of these tasks with AI-integrated software can surely mitigate burnout.

Here’s what AI-powered RPA looks like in healthcare:

  • Automatic record syncing with electronic health records (EHRs) and Electronic Medical Records (EMRs).
  • It reduces operation costs by automating repetitive tasks.
  • AI-based speech recognition technology can transcribe dictations from healthcare providers into text format accurately and quickly.
  • AI algorithms can accurate billing codes automatically upon reviewing medical records, code diagnoses, procedures, and more.
  • It can optimize inventory levels of medical supplies by analyzing supply chain data.
  • AI tools can streamline the process of verifying patients’ insurance coverage and eligibility for services.
  • AI algorithms can automatically schedule appointments based on patient preferences, clinic resources, and more.

Rule-based Expert System

Symptoms are kind of the first point of beginning the diagnosis process, and a Rule-based Expert System can assist in providing accurate and reliable insights based on symptoms alone.

At the heart of Rule-based Expert Systems lie simple yet powerful ‘if-then’ rules. The set of these rules is derived from all sorts of medical knowledge available in various formats from textbooks and research papers to clinical guidelines and expert opinions. These systems conclude or make decisions to provide recommendations upon applying these rules to the given data.

These Medical Expert Systems are developed to diagnose diseases like cholera, malaria, etc.

Examples And Use Cases of AI in Healthcare

Up until now, we have talked about how AI is relatively bringing transformation along with excellence in the healthcare industry. Let’s now talk about examples of AI in healthcare:

AI in Drug Discovery and Development

The pharmaceutical industry plays a vital role in the healthcare sector. As a front-runner beneficiary, their contribution to formulating medicines makes it possible for doctors and clinical staff to treat patients and potentially save lives, which is noteworthy.

One of the expensive investments made by pharma companies is in research and development (R&D) to discover and develop new drugs making it one of the top AI use cases in healthcare. Conventionally considered slow and labor-intensive processes, integrating AI technology into the pharmaceutical drug development pipeline can make this process rapid and effective.

Today, generative AI-driven biotech companies like Insilico Medicine have been successful in creating the first fully AI-designed drug to reach human clinical trials.

Here are some key aspects suggesting the valuable contribution of AI in drug discovery and development:

  • AI algorithms can analyze vast amounts of biomedical data, including genetic information, molecular structures, and clinical trial data, to identify potential drug candidates. 
  • AI-based predictive modeling techniques are capable of accurately identifying the disease targets. It facilitates the development of personalized therapies tailored to individual patients based on the analysis of patient data, including genomics and clinical records. 
  • This technology saves a significant amount of time in developing new drugs by facilitating the repurposing of drugs through the analysis of existing drugs’ molecular structures and biological effects. 
  • AI models can lower the risk of toxicity and adverse effects by predictive analysis of potential side effects of drug candidates, analyzing their chemical properties and biological interactions, and eliminating the potential for safety issues during clinical trials.
  • Last but not least, AI is revolutionizing clinical trials by utilizing its capabilities to create virtual replicas, or “digital twins,” of individual patients. Each of these serves as a dynamic computational model that simulates the physiological and pathological processes occurring within the human body. It enables personalized and predictive insights throughout the clinical trial process.

AI in Personalized Medicine

In contrast to the one-size-fits-all approach of conventional medical treatments, precision medicine (also known as personalized medicine) involves providing tailored treatment upon analyzing a patient’s data such as medical history, genetic information, lifestyle choices, and more.

Instead of focusing on treating the symptoms like the traditional treatment approach, precision medicine shifts the focus to the patient’s individual treatment needs. This is done by using a data-driven approach by considering multiple variables simultaneously to optimize treatment outcomes and minimize adverse effects.

Further, real-time monitoring enables continuous insights into the patient’s health status and treatment response through wearable devices or remote sensors. This allows for timely interventions and adjustments to treatment plans, enhancing patient care.

AI in Medical Imaging

The accuracy of diagnosis is indispensable when analyzing various medical images such as X-rays, MRIs, CT scans, ultrasounds, and more. While radiologists are skilled at accurately analyzing these images, there is always room for human error. Additionally, this process can be time-consuming, especially when abnormalities detected in the image require careful study to accurately identify the underlying issue.

AI, on the other hand, is trained by feeding thousands of images with varying concerns to accurately recognize patterns or abnormalities. They can’t replace radiologists but can certainly assist in saving time and enabling radiologists to make accurate diagnoses, ensuring that no details are overlooked before coming to a judgment.

AI in Genomic Medicine

AI takes personalized treatments to another level with genomic medicine. For starters, genetic data comprises a vast amount of complex information, and advanced computational tools are necessary for analyzing it. 

Even with these tools, time consumption is inevitable. AI powered tools analyzes the genetic data available for identifying genetic markers that are associated with specific traits, diseases, or treatment responses. 

Further, AI models can develop predictive models based on genetic data, enabling doctors to assess an individual’s risk of developing certain diseases or their likelihood of responding to specific treatments. This allows them to take preventive measures, like recommending healthier lifestyles or specific medical treatments, to reduce the risk of these diseases.

AI in Robotic Surgery

The first thing that comes to mind is a fantasy movie scene where doctors operate on a patient using mechanical arms via a computer console to make surgeries more precise and less invasive. This is no longer a fantasy. Surgical robots like the da Vinci Surgical System assist surgeons in performing surgeries, which the surgeon controls from a console.

Precise and minimally invasive procedures are not only preferred by patients but also by doctors, as they enhance the success rate of surgeries. AI-powered robotic surgery systems enable doctors to perform complex procedures with greater precision, efficiency, and safety, ultimately leading to improved patient outcomes.

AI in Patients’ Assistance

These virtual assistants not only make life easier for patients and healthcare providers alike. Here’s how virtual assistants work:

  • They help patients by answering questions, providing reminders, and offering support. 
  • They assist doctors, nurses, and other staff with tasks like scheduling appointments, organizing patient records, and even providing quick access to medical information. 
  • They provide patients with convenient access to healthcare information and support. 
  • They can analyze your activity levels from your smartwatch-derived data and give you tips for staying healthy.

AI in Oncology

Apart from other rare diseases where AI can be useful not only in diagnosis but also in drug research and development, one such disease is Cancer. Here are some use cases of AI in oncology:

  • Accurately studying medical images, such as CT scans, X-rays, MRIs, and more, for recognition (types, stages, and health conditions) or accurate/early diagnosis.
  • Analyzing vast amounts of datasets on medical history, genetic makeup, pathology reports, and more to tailor a personalized treatment plan.
  • Predictive analytics to understand how the patient would respond to the selected type of chemotherapy or different treatment options, potential side effects, and more.
  • Personalized recommendations of treatment types, dosage amounts, and more to improve the chances of successful treatment while minimizing side effects and unnecessary exposure to drugs that may not be effective for specific types of cancer.

AI in Remote Patient Monitoring(RPM)

We have already witnessed the power IoT gives us, using AI for RPM is something similar to you being able to know what’s going on in your hour except this time it’s the healthcare professionals who are able to know what’s going on in your body vitals such as blood pressure, respiratory rate, and more from wherever you are.

A wearable device like a smartwatch or sensors tracks the vitals under observation by the healthcare provider, or general vitals, providing an overview of the overall health condition like heart rate and blood pressure. This data is collected and then analyzed by an AI-powered mobile app connected to the devices for any potential unusual pattern recognition.

Doctors can also access these datasets via the application. If through pattern analysis or any sudden spikes from the set baselines personalized for the patient, the app senses something concerning, and sends an alert to the doctor so they can follow up with it.

AI in Mental Health Support

When it comes to Artificial intelligence and healthcare, it is not just about physical health; AI has been doing wonders in mental health as well.

Mental health issues are often overlooked or unrecognized until significant damage has already occurred, sometimes even leading to tragic outcomes such as suicide. Individuals may struggle silently with mental health challenges, unaware of the severity of their condition or hesitant to seek help due to stigma or lack of awareness.

As a result, these issues can escalate to a crisis point before they are identified or addressed, leaving loved ones and healthcare providers unaware of the struggles the individual is facing.

Here are several aspects highlighting the examples of use of AI in mental health:

  • Machine learning algorithms can identify patterns indicative of depression, anxiety, bipolar disorder, and other conditions, enabling earlier intervention.
  • AI-powered chatbots and virtual assistants provide continuous support and monitoring for individuals with mental health conditions.
  • AI algorithms analyze risk factors, such as previous suicide attempts, social isolation, substance abuse, and changes in behavior, to predict individuals’ likelihood of experiencing suicidal ideation or behavior.
  • AI-driven NLP techniques analyze text and speech data from therapy sessions, support groups, and counseling sessions to extract meaningful insights about patients’ emotions, attitudes, and treatment progress. 

AI in Clinical Documentation

For a doctor in a busy clinic, typing or, worse, writing notes about a patient’s medical history, symptoms, and potential treatment is an important yet time-consuming task that could have been utilized in diagnosing patients.

AI-powered web or mobile applications or even search engine extensions are designed for transcribing the doctor-patient conversation and then analyzing it to provide potential treatment plan recommendations.

In one of our recent projects – AI-powered all-in-one tool for doctors), our team worked on one such feature named ‘Scribe,’ which involves AI-integrated clinical documentation. Upon diagnosing, it even provides (rather generates) a clinical plan to the doctors.

AI in Fraud Detection

Healthcare is one of the industries contributing substantially to global insurance claim fraud, involving fake bills, unnecessary procedures, and more. Healthcare insurance companies receive thousands of claims every day from doctors and hospitals for services provided to patients. This volume makes it harder for these companies to spot the red flags.

AI-powered fraud detection software is trained to analyze the submitted details against the claim for potential red flags that might indicate fraud. Upon identifying suspicious claims, it alerts the concerned department of the insurance company for further investigation. This proactive approach saves a substantial amount of the insurance company’s money.

The Key Benefits of AI in Healthcare

After coming to know about the incredible innovations of AI in the healthcare industry, we are sure that you are already amazed. Let’s learn about the benefits of AI in healthcare industry:

Rapid Diagnosis

AI’s speed in designs is a boon that is capable of saving patient’s lives!

AI algorithms’ capability to analyze medical imaging scans with remarkable speed and accuracy enables the early detection of abnormalities or diseases such as cancer or fractures. This rapid diagnosis allows prompt initiation of treatment, reduces patient anxiety and enhances overall satisfaction with healthcare services.

Furthermore, in emergencies such as stroke, where time is critical, AI-powered diagnostic tools can expedite the assessment process, leading to faster decision-making and better chances of recovery.

Proper Assistance in Surgery

Collateral damage resulting from unsuccessful surgeries can have profound implications for patients and healthcare systems alike. This is when AI-powered surgical robots, such as the da Vinci Surgical System, enable surgeons to perform minimally invasive procedures with greater precision and control.

These systems utilize advanced imaging and sensing technologies to provide real-time feedback, allowing surgeons to navigate complex anatomical structures with unparalleled accuracy.

These bots enhance the capabilities of healthcare providers by providing valuable insights and recommendations based on vast amounts of medical data, ensuring that surgeries are tailored to each patient’s unique needs.

Advanced Accessibility

Individuals in remote or underserved areas often experience a lack of access to quality healthcare services due to geographical limitations or the unavailability of specialized healthcare facilities in their locality.

AI-driven telemedicine enables these patients to consult with specialists and receive expert medical advice without the need to travel long distances, thereby reducing barriers to care and improving health outcomes.

Additionally, AI-driven virtual assistants and chatbots offer immediate support and guidance to patients, answering questions, providing information, and offering self-care recommendations, thereby empowering individuals to take control of their health and well-being from the comfort of their homes.

Key Challenges of AI in Healthcare

Here comes the final segment of AI in healthcare market; the challenges that the healthcare industry has faced:

Keeping Up With Regulations

When it comes to legal issues, numerous confidentiality and privacy laws surround medical records globally, necessitating strict adherence to regulations. Healthcare startups often face challenges in navigating these regulations, especially concerning data sharing among AI systems, which may inadvertently violate laws. In cases where data sharing is legal, patients must provide consent for the use of their medical data for AI-driven medical purposes.

To overcome this challenge, flexibility in regulations is essential. Healthcare startups require rules on medical data acquisition that allow for flexibility while ensuring identity protection.

Human Disagreement

Whenever technology like artificial intelligence enters the healthcare industry, disagreements and differences of opinion inevitably arise. Many patients and medical professionals have reservations about the increasing role of artificial intelligence in healthcare.

Numerous radiologists are reluctant to embrace the idea of robots potentially taking over their work. Similarly, many patients express concerns about relinquishing control of their healthcare to machines and are apprehensive about being treated for their health issues by AI-driven systems.

AI bias or hallucination

While AI technology holds immense promise in healthcare, it also poses significant challenges, particularly concerning bias and misinterpretations. Just as humans are prone to errors, machines can also make mistakes.

For instance, if an AI-based system recommends an irrelevant or incorrect drug to a patient or misidentifies a tumor in a radiology scan, it can lead to disastrous outcomes such as patient injury or severe health consequences, potentially resulting in loss of life.

The Future of AI in Healthcare

The future of AI in healthcare market holds immense promise and the potential to revolutionize the industry in unprecedented ways. As AI technologies continue to evolve and mature, it is clear that they will play an increasingly integral role in shaping the future of healthcare.

One of the most significant areas where AI is expected to make a profound impact is in diagnosis and treatment. In addition to diagnosis, AI is also being used to personalize treatment plans based on individual patient data, including genetics, medical history, and lifestyle factors.

Further, advancements in machine learning, natural language processing, and robotics are paving the way for more sophisticated AI applications in areas such as drug discovery, surgical assistance, and remote patient monitoring. Overall, AI is set to play an increasingly integral role in shaping the future of healthcare.

How Can MindInventory Assist in Your AI in Healthcare Venture?

The integration of Artificial Intelligence (AI) in the healthcare industry has propelled advancements to unprecedented levels. From pioneering innovations to transformative solutions, AI has revolutionized healthcare startups, underscoring its invaluable contribution to the sector. At MindInventory, we recognize the immense potential of AI and are committed to making significant strides in this domain.

Our team is dedicated to developing cutting-edge AI and Machine Learning (ML) solutions tailored to drive revenue growth, cost reduction, and enhance customer experiences in the healthcare sector. Leveraging expertise and experience, Our AI developers for hire aim to deliver impactful solutions that optimize operations, improve patient care, and empower healthcare professionals.

If you’re considering embarking on an AI-driven healthcare venture, we are eager to collaborate with you and be an instrumental part of your success story. Reach out to us today, and let’s embark on this transformative journey together!

FAQs on AI in Healthcare

Is AI in healthcare safe?

Results reveal overall that AI in healthcare is trustworthy. After conducting innumerable surveys and research, researchers learned people had a positive opinion about AI in healthcare. The survey described that 56% believe AI will make healthcare better in the upcoming 5 years, compared to 6% who say it will make healthcare more impaired.

How is AI used in the healthcare industry?

There are five prime use cases of AI in the healthcare industry, such as analysis of medical images, applications for diagnosis and treatment, patient data, remote patient assistance, and drug creation.

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Kumarpal Nagar

Kumapal Nagar is a Python team lead at MindInventory, a certified ethical hacker and proficient in AI/ML and cloud computing platforms, specialized in creating innovative, secure, scalable, and robust performing digital solutions using cutting-edge technologies. With his passion for always being up-to-date with AI/ML advancements and experimenting with AI/ML, he set up a proven track record of success in helping organizations leverage the power of AI/ML to drive meaningful results and create value for their customers.