How AI Medical Diagnosis Changes the Industry: Benefits & Examples

Artificial Intelligence (AI) has successfully made its way into virtually every industry. However, healthcare is a unique sector that requires additional consideration. Dealing with highly sensitive data and vulnerable patients, medical professionals have to be extra careful when using new technologies. At the same time, AI solutions in healthcare can increase the quality and accuracy of medical care and automate some tedious parts of the job. In this article, we will discuss the benefits and challenges of AI in medical diagnosis, explore its use in drug research, and take a look at its ethical implications. 

AI Algorithms in Medical Diagnosis

In order to understand how AI systems can help clinicians with diagnostics, we have to establish two more vital technologies: machine learning and deep learning. While machine learning consumes data to identify patterns, deep learning mimics human neural networks to discover complex relationships in data. Both subsets of artificial intelligence, these technologies are capable of finding connections much faster and more accurately than any human. 

By relying on machine learning algorithms, physicians can analyze medical images within the context of the patient’s genetic data, history, lifestyle, and other factors. Having a comprehensive and holistic analysis can considerably increase the chances of the correct diagnosis. These findings can also aid in treatment plan development. The extensive overview of the patient’s health enables doctors to choose the best medication suited for the concrete case and individual. 

Considering that hospitals already accumulate immense volumes of patient data, utilizing this information to advance diagnostic abilities is the next logical step. In fact, studies show that AI implementation in healthcare can improve the outcomes of medical care by 30 to 40%. At the same time, hospitals will save up to 50% on operational costs. 

AI Applications in Medical Imaging

In this section, we will explore a few medical specialties that already reap the benefits of AI medical diagnosis applications. 

Neurology

Albeit a new industry, AI in medical diagnosis already plays a major role in the detection of various diseases. For example, these algorithms can analyze magnetic resonance images (MRI) to identify biomarkers indicative of Alzheimer’s and Parkinson’s conditions. In the early stages, the changes in the brain are so minute that humans can easily miss the crucial signs. AI systems are more efficient at detecting subtle pattern deviations to provide a timely diagnosis and minimize further damage. 

Oncology

Similarly, oncological scanning can also largely benefit from AI medical diagnosis tools. For instance, these systems are used to identify tumors in breasts, lungs, and prostates and to discern between benign and malignant types. Furthermore, studies reveal that deep learning algorithms show a higher level of accuracy compared to experienced radiologists. AI medical diagnosis mechanisms can also speed up diagnostic procedures, delivering better medical care in a timely manner. 

Cardiology

Finally, AI-based solutions can consolidate data from several scans, including computed tomography (CT), MRI, echocardiography, and more, to offer an exhaustive overview of the patient’s condition. This is applied in cardiology to analyze complex diseases like ischemia. Using MRI and CT in combination with 3D modeling, the rate of successful ischemia detection increases. 

Benefits of AI in Medical Diagnosis

From early disease detection to drug research and advanced genetics, AI medical diagnosis systems offers a slew of advantages. Let’s dive into the benefits of AI in healthcare. 

Early Diagnostics

According to a Pubmed study, medical misdiagnoses range from 1.2% to 35.6% depending on the disease. The reality is that hospitals are understaffed across the countries, especially when it comes to rare seasoned specialists. As a result, physicians are overworked which prevents them from spending enough time with the patient and can even lead to devastating errors. The importance of early detention has been recorded for years and there is sufficient data backing this up. For example, simple pap tests have decreased cervical cancer by 70% since the 1950s. 

Advanced Genetics and Genomics

AI models have also been beneficial in genetics and genomics studies. CRISPR, for example, allows scientists to modify DNA to prevent certain conditions. By targeting a specific location in the DNA, experts can potentially correct mutations to treat hereditary diseases. Moreover, CRISPR can assist physicians in researching various illnesses by creating cells for experimentation. However, genomics is a highly controversial subject due to the utmost precision required. If a scientist targets the wrong part of the DNA, this can lead to life-changing complications. AI-based solutions can swiftly identify the correct target spot and aid researchers in their important work. 

Personalized Medicine

Historically, women have been underrepresented in clinical trials, which has led to limitations in biological understanding and a decrease in drug efficacy. Aside from that, people react differently to the same medication depending on a wide range of factors. Using AI medical diagnosis systems, clinicians will be able to develop personalized treatments based on large amounts of patient data. Such tools can identify patterns between patients and draw conclusions as to what treatment will likely help this particular person. By considering a plethora of factors—including lifestyle, age, biological sex, occupation, and more—physicians can provide more accurate diagnoses and treatments. 

Drug Discovery

With the largest pharmaceutical companies investing hundreds of millions into AI-powered drug research solutions, the industry is gaining more and more traction. These systems help scientists understand diseases better, safely simulate testing, and even create new drug molecules from scratch. The potential of AI-based drug discovery applications is truly remarkable. They can significantly lower the cost of development and production as well as shorten the process of drug creation. 

AI-assisted Diagnostic Support Systems

AI medical diagnosis systems fall under the umbrella of holistic health care (HHC) methodology, which supports the patient’s physical, mental, economic, social, and even spiritual needs. AI-assisted diagnostic applications have already proven to be effective. A study from the National Center for Biotechnology Information (NCBI) shows that such systems yield great benefits for patients with comorbidity, or the presence of two or more illnesses at the same time. By accessing patient data from different sources and considering other factors, AI-powered solutions can improve diagnostic precision. 

Differential Diagnoses

When a new patient arrives at the hospital, doctors begin with a differential diagnosis, which is a list of potential conditions that carry the same symptoms. This is a time-consuming and complex process that requires expertise, experience, and hours or even days of work. Considering how understaffed most healthcare institutions are, doctors oftentimes cannot afford to invest time in exhaustive differential diagnoses. AI in medical diagnosis allows systems that analyze patient data and reduce the number of possible conditions to eliminate wrong diagnoses. Research from NCBI reveals that these systems offer 85% accuracy compared to doctors’ 67%. 

Importance of Collaboration

Despite the advancements of AI for medical diagnosis, medical care is not at risk of being taken over by machines. The best approach involves adopting AI-assisted systems into the job of a clinician. By relying on the solution’s findings and insights, physicians can make decisions faster and more precisely. Although machines are excellent at pattern identification, humans trump them at contextual insight, analytical and creative thinking, and empathy. Doctors can use AI systems to improve and optimize their performance and deliver better care. 

Guide to AI-Powered Drug Research

A notoriously expensive process, drug research has already been benefiting from machine learning methods. Let’s take a look at how scientists develop new drugs using AI-powered systems. 

Understanding the Disease

The process of drug creation begins with a thorough research of the disease itself, including biological origin and resistance mechanisms. Relying on these findings, researchers can identify target proteins for treating the said disease. Using proven practices like short hairpin RNA, CRISPR, and other methods of screening and sequencing, scientists can discover suitable targets to develop effective drugs. However, identifying targets using traditional systems is a lengthy and tedious process that can be made simpler with AI medical diagnosis tools. Machine learning algorithms can process vast amounts of data to quickly find the appropriate target proteins. 

Finding Lead Compounds

Drug compounds are meant to interact with the target protein and alleviate the patient’s symptoms or cure the disease altogether. Discovering effective compounds takes months of diligent screening of millions of potential candidates. Scientists need to make sure the particular compound has the desired effect without presenting harmful side effects. The process of identifying lead compounds can be shortened by involving AI mechanisms that predict the suitability of chemicals and eliminating unfit compounds. 

Identifying Biomarkers

To make sure the patient has this particular disease, physicians check for so-called biomarkers. These are molecules that when discovered in blood serve as definitive proof that the patient has the disease. Although biomarker testing itself is a relatively cheap method, the process of discovering the right biomarker for each condition is lengthy and costly. Similar to searching for lead compounds, this procedure involves checking thousands or even millions of potential molecules. AI in medical diagnostics can automate some of the manual work and accelerate biomarker discovery. 

Speeding Up Clinical Trials

Before putting the drug on the market, scientists need to conduct clinical trials to test the efficacy of the medication. Searching for the right trial candidates takes a long time, which accumulates costs. Machine learning applications in healthcare can swiftly identify the most suitable candidates as well as predict the trial’s effectiveness. If the system gives a warning that the trial design is not likely to yield tangible results, researchers can immediately make necessary changes, thus saving resources. 

Ethical Implications of AI in Medical Diagnosis

No matter how far technology goes in improving medical care, people still crave human-to-human contact when it comes to their health. Many medical procedures require a sense of compassion and empathy, especially when young children are involved. This is why it’s vital to use technology in addition to human medical care, not in place of it. Another ethical implication concerns social gaps and inequalities. Many low-income families don’t have access to the latest technologies which decreases their quality of care. Furthermore, it’s imperative to provide patients with information about the use of AI medical diagnosis systems to let them make informed decisions about their health and treatment. 

Data Privacy and Algorithm Biases

The foundation of AI-driven solutions is patient data. Especially in healthcare, data protection is of utmost importance. Developers of AI medical diagnosis tools are expected to guarantee robust security practices like data anonymization, encryption, and safe storage. Aside from that, historical biases need to be taken into account when developing such applications. This requires dataset diversification to ensure inclusivity and eliminate any discrimination. 

New Legislation

Like any new technology, AI for medical diagnosis requires new regulations and laws. Hospitals need rules and guidelines to control the adoption and usage of AI medical diagnosis, covering such vital factors as transparency, accountability, security, confidentiality, and AI performance assessment. 

Challenges of AI for Medical Diagnosis

From the aforementioned algorithm biases and ethical dilemmas to lack of transparency and interoperability, AI for medical diagnosis poses certain threats and challenges. For instance, there are still some technical flaws that limit the diagnostic capabilities of these systems. Additionally, the merging of AI and medicine requires clinicians to adopt new skills, including data science, statistics, and AI ethics. 

The Black Box Problem

The efficiency of AI medical diagnosis is heavily dependent on the quality of data fed to the system. Referred to as the “black box,” AI systems arrive at the solution without disclosing how they came to this conclusion. This can perpetuate biases and create inconsistent and insufficient data as well as various errors, which affect the results. The lack of transparency behind the AI system’s calculations can widen health disparities and lead to poor care for marginalized communities. 

System Interoperability 

Integrating artificial intelligence in medical diagnosis in often outdated healthcare infrastructure poses a slew of issues. Different data formats, siloed systems, and privacy laws complicate the process even further. Considering seven out of ten hospitals use fax machines as their primary tool to exchange information, many healthcare institutions still operate on legacy software. Before implementing any advanced systems, hospitals need to undergo lengthy modernization. 

Managing New Roles

AI systems are likely to affect all healthcare professionals, especially people holding any managerial responsibilities. Changing the way doctors and nurses perform their duties, such applications may face some resistance. In order to implement AI-powered software across the country, we need to redesign the whole system along with reeducating medical employees. Without a comprehensive approach, hospitals will use different tools or stick to traditional practices creating parallel systems. 

How to Combat These Issues

Nowadays, developers are creating standardized approaches to offer interoperable and reliable AI-powered systems. Additionally, we need to facilitate collaboration between researchers and software developers to ensure transparency and efficiency. Ethical conundrums should also be taken seriously when developing and launching AI in medical diagnostics. 

Future of AI and Final Thoughts

Artificial Intelligence in medical diagnosis can revolutionize the entire healthcare sector. From aiding in accurate and timely diagnoses to automating the analysis of medical images, these systems can deliver personalized treatment plans and reduce errors. AI-powered tools can leverage predictive analytics in healthcare to forecast how the patient would react to a certain drug or treatment. 

Technologies like deep learning, machine learning, natural language processing, and image recognition will continue to remain prominent trends in healthcare. By extracting and merging data from multiple sources, AI-driven applications can offer a holistic and comprehensive approach to diagnosing. 

Intelligent Ortopedia Case

Let’s break down our process using one of our success stories as an example, namely, IoT Telehealth solution powered with AI for diagnostics.

Our client, a global provider of healthcare software systems, decided to build a remote monitoring solution to improve the effectiveness of various musculoskeletal treatment therapies. By relying on IoT sensors and AI capabilities, such a solution can track patient health data in real-time and transmit it to the clinician. Furthermore, an AI algorithm can assist physicians with diagnosis based on gait analysis datasets it was trained on. 

NIX was tasked with developing a SaaS environment that comprises machine learning, IoT, and cloud technologies. The scope included an interface for physicians and patients, machine learning models for data interpretations, a real-time data pipeline, as well as compliance with data protection regulations. 

The cloud-based solution with advanced AI components helps physicians with early detection, improves the quality of the treatment, and lowers costs for both patients and healthcare providers. Moreover, our specialists utilized SDK for 3D modeling which significantly decreased the price of development for our client.

Final Thoughts

Although some challenges persist, the benefits of implementing technology cannot be ignored. However, it’s essential to collaborate with a trustworthy IT organization to create a high-functioning system. NIX is a software development company with a wide range of successful cases with advanced technologies like the Internet of Things, machine learning, and artificial intelligence. 

To leverage AI capabilities in your healthcare institution and improve your diagnostic abilities, get in touch with NIX. We are a team of experienced software developers with numerous successful projects in healthcare and beyond. Reach out to us to discuss your project and build an efficient and useful AI-driven medical application.