AI in Healthcare: The Benefits, Challenges, and the Future

Looking to build Artificial Intelligence software? Then you should know that Artificial Intelligence (AI) is a game-changer in the healthcare industry, transforming the way doctors diagnose, treat, and manage patients. Already in 2022, the global AI in healthcare market was estimated at $15.1 billion, and by 2030 it is expected to exceed $187.95 billion.

Healthcare AI applications, such as clinical decision support systems, cognitive computing, precision medicine, healthcare robotics, and electronic health records and AI, can improve patient outcomes, reduce costs, and increase the efficiency of a healthcare facility.

However, as with any new technology, there are challenges to address when it comes to machine learning algorithms in healthcare, such as ethical issues, data privacy, and regulatory hurdles.

In this article, we will discuss not only the healthcare AI applications challenges but also the business benefits of AI-powered healthcare solutions and the future of AI technology.

AI in Healthcare Overview

Just as GPT-3 and GPT-4 are reshaping the Internet and the way content is handled, AI is now revolutionizing healthcare through precision medicine, robotics, cognitive computing in healthcare, etc. At least, it feels that way.

And while AI in healthcare has tremendous potential, the truth is that the full adoption of AI-powered healthcare solutions has been slow. However, according to Michael Howell, Chief Clinical Officer at Google, more options for faster and easier AI adoption are expected in 2023.

According to the study, one of the drivers of AI adoption in healthcare is cost reduction. It's no secret that healthcare requires investment, while AI in medicine makes healthcare more efficient and accessible worldwide without extra money. AI in healthcare results in solving specific problems as it facilitates more correct diagnosis and treatment.

According to the report:

According to another report, the healthcare AI market will grow to $102.7 billion by 2028.

Statista estimates that the global healthcare AI market is expected to reach nearly $188 billion by 2030, with a CAGR of 37% from 2022 to 2030:

Future and Potential of AI in the Healthcare Ecosystem: Precision Medicine and Access to Health Services at the Spotlight

Below are some areas of AI adoption in healthcare not previously covered.

Multi-Omics Technologies and Precision Medicine

Multi-omic technologies involve integrating and analyzing data from multiple sources such as genomics, proteomics, microbiomics, epigenomics, metabolomics, phenomics, and more. AI plays a pivotal role in deciphering complex patterns within these extensive datasets. Through AI-driven analysis, healthcare professionals can better understand the intricacies of diseases at a molecular level, paving the way for precision medicine.

Genomics and AI

AI can analyze genomic data to identify genetic predispositions to diseases, predict disease progression, and determine the most effective treatments based on an individual's genetic makeup. This facilitates highly personalized treatment plans and targeted therapies.

Proteomics and Metabolomics

Integration of proteomic and metabolomic data with AI can uncover biomarkers, protein interactions, and metabolic pathways relevant to diseases. This information is crucial for developing innovative diagnostic tools and therapeutics.

Digital solutions for healthcare

There is a strong demand in society for ongoing healthcare support. Digital healthcare solutions supported by artificial intelligence are the way out. Let's take a look at some of them.

Telemedicine and remote patient monitoring

Much has already been said about the role of telemedicine in delivering quality healthcare. Yet the role of these AI solutions cannot be overemphasized. AI-based remote patient monitoring devices provide not only virtual consultations with diagnostic capabilities, but also continuous collection and analysis of health data, promptly alerting medical professionals when abnormalities occur.

Mobile health applications (mHealth)

AI-powered mHealth applications can analyze user health data such as exercise regimen, diet, and vital signs. This information can be used to provide personalized health recommendations to encourage healthy lifestyles and disease prevention.

Virtual Reality (VR) and Augmented Reality (AR)

VR and AR technologies powered by AI are enabling realistic medical simulations for training healthcare professionals. Surgeons can practice complex procedures in a risk-free virtual environment, thus improving their skills and confidence.

Access to Health Data

The need for data access solutions is set to intensify in the coming years. Efficient and secure access to health data is critical for informed decision-making, medical research, and healthcare advancements. AI is to continue to help optimize the use of data while ensuring privacy and security.

Interoperability and Data Integration

AI facilitates interoperability by developing standard formats and protocols for seamless data sharing among disparate healthcare systems. This integrated approach ensures that healthcare providers have a comprehensive view of a patient's health history, resulting in better-coordinated care and improved outcomes.

Federated Learning

Federated learning, an AI technique that allows model training across multiple decentralized sources of data, ensures data privacy collaboratively. Hospitals and research institutes can collectively improve AI models without handing over identifiable patient information, which promotes the adoption of privacy-oriented AI.

Benefits of Artificial Intelligence in Healthcare

Let's take a look at how AI technology is enriching the healthcare industry.

Easy real-time access to complex and important data

Healthcare data analytics is one of the most compelling reasons why AI is being adopted in medicine. Machine learning algorithms in healthcare are expected to be able to process large amounts of data quickly and thus realize the potential of AI. How can this be done?

• Electronic health records and AI make it possible to find and retrieve the necessary data on a particular patient faster, which reduces the time from problem detection to resolution;
• Clinical decision support systems are another way to apply AI in the healthcare system, allowing for data-driven decision-making for each clinical case;
• AI is being introduced into medical systems to track blood sugar, blood pressure, and other vital signs, giving doctors and patients a clear picture of their health status and how to improve it;
• The growing use of AI in healthcare allows for collecting, storing, and analyzing data on disease risk, which looks like the next game-changing step in the evolution of AI in healthcare.

AI Can Increase Efficiency in Healthcare Diagnoses

Predictive analytics for healthcare makes it possible to make accurate diagnoses and predict the most likely future health problems. To do so, artificial intelligence analyzes millions of diagnoses and symptoms of previous patients and makes predictions.

The result of the application of precision medicine and AI is end-to-end AI-powered healthcare software and mobile applications that can predict genetic diseases or future health risks based on the patient's and his/her relatives' health data.

Here are a few examples of how AI-powered software can be used to check symptoms and get a diagnosis:

• Ada Health: This AI-powered symptom checker uses natural language processing in healthcare to help users identify possible causes of their symptoms.
• Buoy Health: Buoy is an AI-powered symptom checker that asks users questions about their symptoms and medical history to help them identify possible causes.
• SkinIO: SkinIO is an app that uses artificial intelligence to analyze photos of moles and other skin spots to help detect skin cancer.

Why is AI leading the way? Let’s turn to the use of deep learning AI technology to solve the problem of breast cancer risk prediction. Here are the advantages of AI:

• Artificial Intelligence algorithms can be trained on a much larger set of images compared to clinicians. We are talking about a million or more radiologic images.
• No extra cost is needed to use AI algorithms, just the hardware.

While AI's huge impact on diagnostics is yet to be seen, it can already improve patient outcomes by 40% and reduce treatment costs by up to 50%. There has also been a study showing that Artificial Intelligence can recognize skin cancer earlier and better than a professional. The study involved 58 international dermatologists on one hand and deep learning on more than 100,000 images on the other hand to detect skin cancer. AI did a better job.

The AI-enabled diagnostics market is expected to reach over $3.8 billion by 2025. These figures are a clear sign that AI in healthcare will continue to be a major driver of the industry.

Cost savings through AI-powered healthcare solutions

And, of course, it is hard to ignore the cost-effectiveness of AI in healthcare. AI can make healthcare more accessible and cost-effective for everyone involved. AI technologies in healthcare, such as computer vision in medical imaging, natural language processing in healthcare, healthcare data analytics, etc., can help save up to $16 billion in costs associated with hospital readmissions. Currently, 50% of MedTech companies see the transformation of functions through digital and information technologies as a priority.

AI makes healthcare more accessible

The global healthcare system differs from country to country in terms of accessibility, technology, and efficiency. The quality of health and life depends on whether people in a country have access to health care. There is a ranking of countries in terms of health and access to health care in 2023, measured by the Health Index. Singapore leads the ranking, followed by Japan and South Korea:

Access to healthcare affects life expectancy. Even now, in the 21st century, life expectancy in some African countries is less than 60 years. In contrast, life expectancy in Europe and Japan is around 80 years and above. This inequality is often associated with the low availability of medical services for the former.

The application of artificial intelligence in healthcare through technologies such as computer vision in medical imaging, predictive analytics, and natural language processing will allow more people to access the healthcare system. Thanks to AI in healthcare, different organizations around the world can join forces to help more and more people in need.

In addition, the application of AI technology in healthcare means that the basic right of every person to quality healthcare will be realized.

AI can help make healthcare operations more efficient

Improving efficiency in healthcare with AI-based solutions extends from office tasks to patient care. AI eliminates manual patient data entry, providing hands-free voice recording while leaving more time for face-to-face interaction with the patient. Billing, transcription of records, and data sharing between departments and healthcare facilities are also simplified with AI.

So are communication issues. AI therapy has become so hot that 40% of Americans choose it over a face-to-face visit with a real therapist. Using chatbots to communicate with patients not only frees doctors from mundane work, but also allows them to answer patients' questions quickly and clearly, as well as help patients schedule doctor's visits.

Now turn to a not so obvious task. It is about reducing errors in determining the dose of medications, particularly when taking insulin. Using wireless sensors, AI systems can monitor whether a patient is taking medication correctly. This works like a wi-fi router - the system monitors medication intake in the background and reacts to medication errors.

Surgery with healthcare robotics

Surgery is one of the most complex and challenging processes in medicine. Every cut and movement literally affects human lives. Artificial Intelligence and machine learning algorithms in healthcare have combined to deliver robots capable of performing high-accuracy surgeries. Robots do not get tired, are free of emotions and prejudices, and AI-driven healthcare robots' movements can be programmed and controlled. This is clear proof of the benefits of healthcare robotization.

Below are two ways healthcare robotics can be applied in surgery:

• Da Vinci Surgical System: This is a robotic surgical system used to perform minimally invasive surgeries. The system consists of a console operated by a surgeon and robotic arms that hold and manipulate surgical instruments. The da Vinci system is used to perform a wide range of surgeries, including prostatectomies, hysterectomies, and heart surgery.
• ROSA (Robotic Surgical Assistant): ROSA is a robotic surgical assistant used in neurosurgery. The system consists of a robotic arm mounted on a mobile base. The doctor uses a joystick to control the robotic arm, which can be used to guide surgical instruments during brain surgery. The ROSA can be utilized to assist in a wide range of neurosurgical procedures, including deep brain stimulation, epilepsy, and brain tumor surgery.

Greater trust from patients

As AI is still being introduced into the healthcare system, it's only natural that patients and their families are somewhat wary of machine learning algorithms in healthcare, predictive analytics for healthcare, or healthcare robotics. And there are reasons for this, including concerns about data security and diagnostic accuracy. People are used to trusting people, not impersonal robots. These are the issues that AI-powered healthcare software is designed to address.

Nevertheless, patients already notice more personalized service and round-the-clock access to information and treatment provided by AI solutions. Patients like this fact.

Healthcare AI Applications Challenges

Despite the boom in the use of artificial intelligence in healthcare, there are still concerns:

• 52% of U.S. healthcare executives said AI in healthcare could lead to privacy threats;
• 45% of respondents are worried that AI may cause security risks;
• 35% of healthcare executives expressed concern about machine bias.

Indeed, there are concerns to overcome. Below you can find some insights into the risks of AI to healthcare:

Data bias

Since AI-powered healthcare solutions are built using machine learning and predictive analytics algorithms, there is a risk of data bias. If the data used to train these algorithms is incomplete, biased, or inaccurate, the algorithms may produce inaccurate results. For example, if an algorithm is trained on data that is largely related to one population group, it may fail to accurately predict results for other populations. This could lead to a misdiagnosis or an incorrect treatment plan.

Privacy and security risks

Medical data is highly sensitive, and as more and more healthcare organizations implement AI-based solutions, there is a risk of data breaches and cyberattacks. Hackers may try to exploit vulnerabilities in AI healthcare systems to gain access to patient data, which could harm patients and damage the reputation of healthcare organizations.

Ethical issues

AI-based healthcare solutions raise a number of ethical issues, including transparency, accountability, and patient autonomy. For example, if machine learning algorithms in healthcare are applied to make end-of-life treatment decisions, questions may arise about who is responsible if the algorithm makes the wrong decision.

Implications for healthcare workers

As AI-based solutions become more widespread in the healthcare industry, there is a risk of job displacement and a change in the role of healthcare workers. For example, intelligent virtual assistants in healthcare can be applied to automate tasks previously performed by healthcare professionals. This can lead to job loss, reduced job satisfaction, and professional burnout. However, according to Google's Chief Clinical Officer Michael Howell, AI will not replace doctors and medical staff but will be a tool that will complement and assist them.

Quality and safety

AI-powered healthcare solutions must be thoroughly tested and approved to ensure their safety and effectiveness. There is a risk that some AI software solutions may not be thoroughly tested or approved for use, which could result in patient harm.

Examples of how these risks may manifest themselves in practice include the following:

• Machine learning algorithms in healthcare that are used to predict readmissions may be biased toward certain patient groups, resulting in incorrect predictions and higher readmission rates among these groups.
• A healthcare organization that relies on AI-based healthcare imaging solutions may face a data breach that could lead to the theft or manipulation of patient data.
• Intelligent virtual assistants used for patient management may make incorrect decisions about patient care, which can lead to patient harm.
• Healthcare professionals may lose their jobs or experience decreased job satisfaction as a result of implementing an AI-driven solution in their workplace.
• An untested AI-based solution may be used to diagnose a patient, which can lead to an incorrect diagnosis and treatment plan.

To mitigate these risks, healthcare organizations should:

• Ensure that any AI-based solutions they implement are thoroughly tested and validated;
• Adopt adequate security measures to protect patient data;
• Provide training for healthcare professionals to handle AI technologies effectively;
• Be open about how AI is leveraged in healthcare.

In addition, AI in healthcare can help with employee management. If you need to structure data and optimize the actions of healthcare workers, you can build Artificial Intelligence software, such as an employee management system.

This can be a web-based ATS, CRM, ERP, or other employee management app designed to help you better comprehend your employees and their needs. Recruiting software development with AI is another way to get the most out of artificial intelligence and machine learning algorithms.

Future of AI in Medicine

Here's what the next steps in implementing AI-based healthcare solutions might look like.

Real-life data and experience in the spotlight

The use of artificial intelligence in healthcare is expected to grow, driven by the expansion of the range of data used. The emphasis is on the use of data collected outside the hospital using sensors and AI-based wearable technologies. Thus, real-world experience and data will be increasingly leveraged. This practice seems to be helpful in the treatment of chronic diseases.

Legal issues may arise

As for legal issues, several possible obstacles may arise in the future:

• Intelligent virtual assistants in healthcare generally lead to poor patient outcomes;
• A specific AI healthcare application or system has a bad impact on the patient's health, for example, due to inaccurate prognosis and diagnosis.

AI as part of a multi-component healthcare system

It is predicted that clinical activities, such as teaching and medical practice, will be covered by AI and machine learning solutions. It can even be minimal AI-assisted tools or meta-processes. However, all of them, like desktop adoption, are on their way to becoming the new normal for the healthcare industry in the future.

Text-to-image and image-to-text processing

The future of healthcare is closely tied to AI capabilities, such as converting images (scans or videos of surgeries) into text descriptions. Conversely, each medical professional can describe a task in words, and AI tools will transform these instructions into correct diagrams and images. This is expected to be the result of a mix of generative and search AI and will significantly speed up many tasks.

Top 6 Trends for AI in Healthcare

Now that we’ve clarified the benefits, challenges, and future of AI in medicine, it’s time to find out the current trends in the use of AI in healthcare. The most significant ones are outlined below.

Sentiment Analysis in Healthcare

Sentiment analysis in healthcare involves analyzing patient feedback, comments, and reviews to determine their emotional state. This technology relies on natural language processing in healthcare and machine learning algorithms to classify and quantify emotions such as happiness, sadness, and so on. Sentiment analysis can help healthcare providers gain valuable insights into the patient experience, which can improve patient satisfaction, quality of care, and outcomes.

Furthermore, as an AI technology, sentiment analysis can help identify areas for improvement in healthcare services and enable healthcare providers to respond to patient concerns in real-time.

Text Classification in Medical Research

Text classification in medical research is a technology based on machine learning algorithms to categorize and analyze large volumes of unstructured medical text data.

By organizing and categorizing data into appropriate categories, text classification can help researchers identify patterns and trends that may be difficult to detect manually, ultimately leading to more efficient medical research. Text classification can be leveraged to improve healthcare research in a variety of areas, including drug discovery, disease diagnosis, and treatment planning. The global text analytics market is estimated to grow to $27.63 billion by 2028.

Chatbots for Patient Communication

Chatbots for patient communication are AI-powered virtual assistants that can communicate with patients in natural language. They use machine learning algorithms and natural language processing to interpret patients' questions and provide useful answers.

Chatbots can plan doctor's appointments, answer basic medical questions, and provide support during treatment. They can also categorize patients by asking them a series of questions to determine their most pressing medical needs and refer them to the appropriate healthcare services. Chatbots can be of particular use to patients with chronic conditions who need to interact with healthcare providers frequently.

Named Entity Recognition for Medical Coding

Named Entity Recognition (NER) for medical coding is a technology leveraging machine learning algorithms to analyze unstructured medical text data and automatically identify and categorize important information such as patient names, diagnoses, treatments, and medications. By automating the healthcare coding process, NER can significantly reduce the workload of healthcare professionals and increase the accuracy and efficiency of medical coding. According to the Medical Coding Global Market Report 2023, the medical coding market is predicted to grow to $24.81 billion by 2027.

AI-based Emotion Detection in Patient Care

Emotion detection in patient care is an advanced technology that uses artificial intelligence to identify and interpret human emotions during medical consultations or treatment. It relies on sensors such as cameras, microphones, and wearable devices to capture patients' emotional reactions and machine learning algorithms to analyze the data and classify their emotional states.

Emotion detection in patient care is of particular value in the treatment of mental illness, where the emotional state can have a significant impact on patients' recovery. By analyzing the emotional state of patients during therapy sessions, healthcare professionals can customize treatment to better meet the needs of patients.

Clinical Trial Optimization with NLP

It is an AI-powered technology for processing unstructured clinical trial data such as electronic health records, clinical notes, and trial results. This artificial intelligence software solution can help researchers identify patterns and insights that may be difficult to detect manually, resulting in more accurate clinical trials.

Clinical trial optimization with NLP can help identify patients who are suitable for specific trials, speed up the patient selection process, and reduce clinical trial costs. According to Data Bridge Market Research, the AI-based clinical trials market is estimated to grow to $5.55 billion by 2029. NLP-based clinical trial optimization is expected to simplify the drug development process, improve patient outcomes, and reduce healthcare costs.

AI in Healthcare: Your turn

Now that you know the current situation with AI in healthcare, you have a choice: build Artificial Intelligence software from scratch or consider other options to contribute to the industry.

Today, the demand for AI software solutions in the healthcare sector is growing, as there is a market need for them. To get in the game, it makes sense to consider the following steps:

• Decide what kind of AI-powered healthcare software solution to create - a web-based app or mobile app, an MVP or a full-featured platform, etc;
• Turn to IT outsourcing and hire software developers with experience in developing AI software for healthcare;
• Operate within a Staff augmentation collaboration model and follow an Agile project management approach for greater transparency;
• Estimate software development time and budget, and then onboard a hired development team.

If you have any questions about how to create AI software or how to build an AI-based SaaS system, don't hesitate to contact our experts.

Bottom line

As you may have seen, integrating AI into healthcare is a future that is already taking place. AI has revolutionized how diseases are diagnosed and treated and improved patient outcomes. For businesses and enterprises in the healthcare industry, creating a software solution with artificial intelligence can lead to significant operational efficiencies and cost savings. However, you should know that creating such a software solution requires expertise in both healthcare and AI technologies.

If you're a business owner looking to develop an AI solution for healthcare, DDI Development can help. You can hire software developers with the expertise needed to navigate the challenges of AI in healthcare and develop cutting-edge AI-powered healthcare solutions. Don't miss out on the potential benefits of AI in healthcare - let us help you navigate to the right fit for your needs.