Machine Learning & AI for Healthcare in 2024

avcontentteam 05 Feb, 2024 • 12 min read

Introduction

Machine learning (ML) and artificial intelligence (AI) are two of the most widely used technologies in the world. These technologies continually evolve and find newer use cases; however, ML and AI in healthcare are not very new. The first time AI applications were used in healthcare was in the 1970s. Since then, AI-powered applications have developed and adjusted to alter the healthcare sector by lowering costs, enhancing patient outcomes, and raising overall productivity.

As per a survey conducted by AI in Healthcare, over 40% of industry experts already utilize AI and machine learning on a regular basis.

The market for machine learning and AI for healthcare is now worth several billion dollars. In the coming years, it is expected to expand tremendously. Healthcare practitioners will increasingly rely on them to help them evaluate huge volumes of patient data and develop more precise and individualized diagnoses and treatment plans. For all we know, with machine learning and AI, Sepsis may have met its match in an algorithm that predicts its onset one or two days in advance! This is how specific ML-based progress can be made in the healthcare industry.

With AI’s continued development, healthcare is in for a revolutionary change, with new advancements in disease detection, tailored therapy, and medication discovery possible.

This article talks about the same. Read on to learn more about how these technologies impact the healthcare industry.

Table of Contents

Top 5 Areas of Impact of Machine Learning, Data Science, and AI in Healthcare

Data science in healthcare, including machine learning and artificial intelligence, has significantly impacted the healthcare industry by assisting in drug discovery, disease prevention, clinical research, clinical decision support, medical imaging, and diagnostics, and much more.

1. Drug Discovery and Production

Machine learning and AI for healthcare institutions are also responsible for accelerating drug discovery, development, and production. Utilizing the previously collected medical data on active components in drugs and combinations of these components, ML algorithms can map each of them to counter diseases. For instance, Xtalpi, Massachusetts, combines AI, quamtum physics and cloud computing on its ID4 platform to design drugs.

ML in Drug Discovery and Development

Source: PubMed Central

2. Diagnostics

Machine learning and AI for healthcare have significantly impacted the diagnostics vertical of the healthcare industry. These algorithms can be trained to analyze medical images like CT scans, MRI images, X-rays, etc., to identify early signs of diseases like cancer. A recent meta-analysis found that ML algorithms perform the same tasks as human experts, with 87.0% sensitivity and 92.5% specificity for deep learning algorithms compared to 86.4% sensitivity and 90.5% specificity for medical professionals. This helps in the detection of a complication in the early stages and better decision making on the part of medical professionals.

ML and AI in diagnostics

Source: Encora

Besides ML algorithms, AI is highly beneficial in studying and identifying genetic mutations to see if there are chances of developing diseases inherited from ancestors. Additionally, de-identified, HIPAA-compliant data that does not contain any personally identifying information (PII) can be mined by AI to extract information and spot patterns.

3. Personalized Medicine

Every medical case is unique, making medicine a complex and resource-intensive specialty. Individuals generally have multiple conditions that need to be treated simultaneously. So, tough and complementary decisions must be taken to create an effective treatment plan that accounts for drug interactions and minimizes side effects for all the issues. This is where machine learning and AI for healthcare becomes essentially indispensable.

For instance, the Cleveland Clinic in Ohio uses AI to personalize healthcare plans with IBM Watson. It gathers trillions of data points on patient records and streamlines their precision medicine experience.

Future Medicine with ML-based Personalized Medicine

Source: Pressbooks

4. Clinical Research

Clinical trials and research are time-consuming and expensive operations. It makes sense for new medications and medical techniques to be tested for safety before being used broadly, otherwise leading to a loss of billions of dollars. There are circumstances, though, in which the solution must be made public as quickly as feasible. In circumstances like the onset of the COVID-19 pandemic in 2019 or that of The Spanish Flu in 1918, information about a potential outbreak becomes the need of the hour. Machine learning algorithms have been proven significantly vital in gathering information at such a large scale.

AI and ML in Clinical Trials

Source: Clinion

5. Disease Prevention, Outbreak, and Monitoring

Another area of impact of machine learning and AI for healthcare is disease prevention and monitoring and predicting potential outbreaks. By analyzing patient data, algorithms can identify disease risk factors and help doctors intervene before symptoms appear. For example, machine learning can predict which patients are at risk of developing diabetes and provide personalized recommendations for diet and exercise to prevent the disease.

AI can also monitor patients’ health remotely, alerting doctors to changes in patients’ conditions before they become serious. Moreover, the most recent COVID-19 outbreak has made everyone witness how underprepared the healthcare industry is if a disease of such magnitude breaks out. Along similar lines, ML and AI-based tools can help detect the early signs of an epidemic, ultimately preventing a pandemic.

Disease Prevention & Management, Outbreak Prediction | ML and AI for Healthcare

Source: Lancet

Top 10 Applications of Machine Learning and AI in the Healthcare Industry

Below is a list of the top 10 machine learning and AI applications for the healthcare industry. Read on to learn more about these real-world applications.

1. Robotic Surgery

Surgery is very precision-based and a life-saving process. When surgeons are in an OR, they must also have their eyes out for each incision they make or every step they perform. To aid surgeons in this highly-sensitive process, machine learning and AI for healthcare have enabled the medical field to develop and use collaborative robots in the process. The trajectory, depth, and speed of surgical robot movements can be precisely controlled. Because they can function without becoming tired, they are especially well-suited for treatments that call for the same repetitive movements.

Application

Robotic surgery is used by the non-profit academic medical facility Mayo Clinic in the US for various treatments, including urologic, gynecologic, and colorectal surgeries. The hospital uses the da Vinci Surgical System. This robotic-assisted surgical device enables healthcare professionals to carry out complex operations more accurately and precisely.

Robotic Surgery at Mayo Clinic | ML and AI for Healthcare

Source: Mayo Clinic

2. Precision Medicine

Precision medicine refers to the medical treatment plans that have been made for certain people or groups after genetic or molecular profiling to maximize effectiveness and therapeutic benefit. AI systems in healthcare organizations can play a key role in precision medicine by analyzing large amounts of complex data to identify patterns and associations that can inform personalized treatment decisions. By doing so, they provide clinical decision support by identifying treatment plans likely to give more effective health outcomes for individual patients.

Application

To help medical professionals plan radiotherapy and surgery, Microsoft’s Project InnerEye uses computer vision and machine learning to distinguish between malignancies and healthy anatomy using 3D radiological images. Microsoft aims to create medicine specifically suited to each patient’s needs using this AI-based method.

Microsoft Project InnerEye with Computer Vision

Source: Microsoft

3. Electronic Health Records (EHR)

The term “electronic health records” (EHR) refers to an electronic version of a patient’s medical records kept up to date by clinicians or healthcare providers. The patient records may include all the administrative and clinical data pertinent to that patient’s care. Maintaining records of medical history is one of the most standard machine learning and AI for healthcare use cases, as these technologies help in managing and analyzing medical data to give better insights into the patient’s condition.

Application

The Massachusetts General Hospital Clinical Data Science Center is a founding technology partner with NVIDIA. The Center aspires to act as a focal point for artificial intelligence (AI) applications in healthcare for “disease detection, diagnosis, treatment, and management.” The center is using the technology in radiology and pathology, planning to expand it to EHR and genomics.

EHR | ML and AI for Healthcare

Source: Appinventiv

4. Genome Sequencing

Genomics is a branch of molecular biology that caters to genomes’ (set of chromosomes in a gamete/microorganism) structures, functions, evolution, and mapping. Machine learning and AI for healthcare have significantly contributed to genomics and genome sequencing. They have helped to accelerate the pace of scientific discovery in this field. They help in genome assembly, identifying differences between genome variants, developing new drugs and therapies based on genomic data, and much more.

Application

An ML-based method for identifying uncommon genetic illnesses has been adopted at the Hospital for Sick Children in Toronto, Canada. The AI-based MendelScan tool leverages ML algorithms to analyze genetic data and looks for probable disease-causing mutations.

It can also estimate the probability that a specific mutation is to blame for a patient’s symptoms by comparing their genomic data to a database of known disease-causing variants.

Genome Analysis using AI and ML | ML and AI for Healthcare

Source: SickKids

5. ML-based Behavioral Modification

Since the widespread use of machine learning and AI for healthcare, many companies have sprung up inpatient treatment, cancer prevention, detection, and, more recently, behavioral modification, among other areas. Behavior modification is a psychotherapy strategy mainly employed to stop or lessen inappropriate psychological conduct in children and adults. Machine learning algorithms can analyze past behaviors and make recommendations for necessary modifications. For instance, an ML algorithm could predict the likelihood of a person smoking based on their demographic and behavioral data.

Application

To assist users in monitoring and spotting early indicators of skin cancer, the smartphone application SkinVision leverages machine learning and AI for healthcare for behavioral modification. The app analyzes photos of skin lesions using algorithms and gives users a risk estimate of their skin lesions. The ML algorithm then examines the image and evaluates the lesion’s risk using a database of skin lesions.

SkinVision ML and AI Behavioral Modification

Source: Daily Mail

6. Medical Imaging Diagnosis

With advancements in medical imaging using newer medical devices for CT scans, MRIs, X-rays, etc., radiologists can provide better images of where the problem lies. Researchers claim that hundreds of histopathology images are routinely evaluated and labeled by pathologists to determine whether a patient has some issue. Yet, there may be lesser accurate diagnoses due to their increased workload on average. This is where machine learning and AI for healthcare come in to help assess the complications uncovered by medical imaging. For instance, AI could identify cardiovascular issues like left atrial enlargement or automate tasks like aortic valve analysis, carina angle management, and pulmonary artery diameter.

Application

The University of California, San Fransico (UCSF) Medical Center uses machine learning models and AI technology for intelligent imaging. The hospital has developed an AI-powered system called “Clinicai,” which uses deep learning algorithms to analyze medical images and provide diagnostic recommendations to radiologists.

UCSF AI Center for Intelligent Imaging | ML and AI for Healthcare

Source: UCSF

7. Oncology Research

Globally, there is a strong correlation between cancer and mortality. Although substantial improvements have occurred over the past few decades, oncology treatments and care still need to be there. Machine Learning and AI for healthcare have helped this domain give cancer patients a better chance at preventing and managing cancer. Applications of AI in oncology include but are not limited to improving clinical practice, enhancing cancer research, better-comprehending tumor features, and optimizing cancer patient outcomes and treatment response prediction. Moreover, NLP (natural language processing) can also help to identify relevant articles and research papers, reducing the time and effort required to look up treatment options.

Application

Pfizer, one of the world’s leading pharmaceutical companies, has collaborated with IBM Watson AI to advance its oncology research. The collaboration between the two is focused on accelerating the drug discovery process, immuno-oncology research, and improving the outcomes for cancer patients.

IBM Watson and Pfizer

Source: HBS Digital Initiative

8. Improved Radiology

Radiology is one of the fields where machine learning and AI for healthcare are most in demand. Many discrete variables, like tumors, lesions, foci (benign or malignant), etc., may appear in medical image analysis at any time. Diagnosing and identifying these factors is more straightforward since the algorithms learn from the various available samples. The challenging part is that these complex equations cannot be used to simulate their locations accurately. This is where ML and AI step in.

Application

AI-enabled radiology is used in numerous healthcare institutions like Mount Sinai Health System in NYC. Here AI is used as a “second opinion” to a radiologist for detecting COVID-19 in patients’ CT scans. The healthcare system is designed and developed by health-tech startups like Synapsica, which provide holistic AI-enabled diagnostic radiology technologies.

AI-enabled Radiology | ML and AI for Healthcare

Source: EurekaAlert

9. Clinical Trial Optimization

There are several potential uses for machine learning and AI for healthcare in research and clinical trials. Clinical studies can take years to complete, cost much money and effort, and are labor-intensive. Researchers can create a pool of possible clinical trial participants using ML-based predictive analytics to discover individuals from various data points, including prior doctor visits, social media, etc. Machine learning is also used to determine the appropriate sample size to test, ensure real-time monitoring and data access for trial participants, and harness the power of electronic records to minimize data-based errors.

Application

Medidata, a unified life science platform, utilizes machine learning and AI for healthcare to combine data and AI-powdered insights to optimize clinical trials and patient-focused breakthrough therapies. This platform also benefits from Rave EDC (electronics data capture) to harness vast amounts of big data for trials.

Medidata AI Clinical Trials

Source: Medidata Solutions

10. Predictive Analytics

Predictive analytics of medical data is another common application of machine learning for healthcare. AI and ML algorithms can analyze massive amounts of datasets to identify patterns and trends and then use these insights to make predictions about future events. Additionally, this domain caters to complex data, and AI/ML automation can definitely make the process more accurate and efficient.

Application

Predictive analytics is a tool that Kaiser Permanente, a healthcare organization and insurance provider, uses to identify people at risk of developing chronic illnesses like diabetes or heart disease. KP HealthConnect, the company’s platform for predictive analytics, leverages patient data to detect risk factors by analyzing a huge amount of data and then offers customized interventions to assist patients in managing their health.

KP’s Tech Journey | ML and AI for Healthcare

Source: Kaiser Permanente

No Code ML for Healthcare Industry: How No Code ML can Accelerate Growth of Healthcare Companies?

Now that you are aware of how machine learning and AI for the healthcare sector can help medical professionals in several ways, like data analysis, drug discovery, patient health management, etc., let us move to a particular segment, no-code or low-code ML solutions. No Code ML or machine learning platforms that require little to no coding experience can be a game-changer for the healthcare industry. Below are some ways in which no-code ML solutions can accelerate the growth of healthcare companies.

  1. With No Code ML, healthcare companies can develop and deploy machine learning models much faster than with traditional coding methods.
  2. More intuitive for non-technical users, like product managers, to still utilize ML capabilities in their medicinal practice without writing code or developing their own system.
  3. They help companies differentiate themselves in a crowded marketplace and build a loyal patient base by improving patient outcomes.
  4. They can help healthcare companies develop personalized treatment plans for patients by analyzing their individual health data.

If you want to know more about how no-code or low-code ML and AI can benefit this sector and others, you can refer to articles on Analytics Vidhya. The platform offers a wide range of resources for data science enthusiasts, including tutorials, articles, forums, and online courses. The platform is focused on making data science accessible to everyone, regardless of their experience or background. With an active community of machine learning and artificial intelligence experts, the platform offers potent learning materials to make you an expert in this domain.

Conclusion

Applying machine learning and AI to healthcare has enormous and fascinating potential. By utilizing these technologies, healthcare organizations can speed up the development of cures and treatments that save lives, enhance patient outcomes, and lower costs. A better diagnosis and more individualized care can result from their ability to evaluate vast volumes of patient data rapidly and reliably. Additionally, the application of ML and AI in healthcare can speed up the development of novel treatments and therapies for patients by enabling more successful and efficient clinical trials and medication discovery. Summing it up, machine learning and AI is a boon to healthcare innovations, improving patient care and saving more lives.

Frequently Asked Questions

Q1. What are the applications of AI in healthcare?

A. Artificial intelligence (AI) has several applications in the healthcare industry. Some of these are

  • Drug discovery,
  • Personalized medicine,
  • Medical image analysis,
  • Predictive analytics,
  • Virtual nursing assistants,
  • Remote patient monitoring, etc.

Q2. What is the difference between machine learning and AI?

A. Machine learning and artificial intelligence are closely related. However, there are some differences. AI is a broader concept that caters to the development of machines programmed to think and act like humans, whereas ML is a subset of AI that deals with the training phase of these machines. ML uses historical data to learn and enable machines to make better decisions.

Q3. What are some concerns about AI in healthcare?

A. Like any other technology, AI also comes with certain limitations and concerns. Some of these concerns are

  • Ethical Concerns: about liability and accountability of a patient’s health if AI systems make errors.
  • Lack of Transparency: some AI algorithms are tough to understand, making them challenging for healthcare professionals.
  • Over-Reliance: there is always a risk that healthcare providers could become too reliant on AI systems, compromising their own knowledge and efforts.
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