While AI is shaking many important sectors including energy, aerospace, supply chain, and construction, healthcare is no exception. AI-based healthcare solutions are becoming widespread, and so is the increase in market value of these products. Among these products, AI-based diagnostic products are bringing the most remarkable change thanks to AI’s ability to analyze large datasets which helps it accurately diagnose diseases. This is a clarion call for the whole health sector since changes in this sector may turn out to be so drastic that AI may replace expert physicians in the future!
So you may wonder how AI works in the health sector. A simple explanation is AI is like a superhuman brain. AI-based healthcare programs mimic the function of the human brain which learns from experience, analyzes large datasets, identifies the pattern, and finally makes a decision like a human.
How AI is used in Diagnosing diseases
AI has shown its impressive potential in diagnosing diseases. Traditional diagnostic approaches are less efficient than AI, though not outright ineffective. The shortcomings of traditional approaches are they are slow and time-consuming. For example, reading patients scan reports requires time and skilled technicians. In contrast, AI can scan reports and reach a decision instantly. AI is helping in diagnosing diseases in the following healthcare areas
Imaging and Radiology
Radiology is about analyzing images of patients’ internal organs, including the whole body, brain, chest, breast, mouth cavity, and abdomen. Unlike human radiologists, AI’s algorithm is more efficient in analyzing images of internal organs, detecting suspicious points, finding patterns, and providing accurate diagnostic results.
Pathology
Pathology involves diagnosing diseases by analyzing patients’ cells, tissue, or body fluids. The image analyzers convert the images of histology slides into data. Images of histology slides are quite large. So the data resulting from these slides is also quite large and complex. Since AI is capable of performing high-performance data computing, it can help pathologists identify diseases.
Genomics
AI is a powerful data-analyzing tool. It is designed to think and learn like human brains. Armed with superb data analysis ability, AI can analyze previous gene model data and identify heredity, and gene-related disorders.
Benefits of AI Medical Diagnosis
Like in many other sectors, AI’s involvement in healthcare was inevitable. However, introducing new technology, especially in diagnosing diseases requires confidence, and assurance that AI will not disappoint health professionals. AI has probed its efficacy in diagnoses of diseases in the following ways
Accuracy and Precision
The conventional diagnosis process practiced over the past 100 years involves manually inspecting disease images under a microscope. This process is labor-intensive, time-consuming, and prone to error. The introduction of AI in diagnosis means AI will do the heavy lifting of disease image analysis by using its machine learning, and algorithm techniques which in turn will deliver quick diagnosis results. In addition, AI in diagnosis means minimal human intervention, zeroing the likelihood of human error in diagnosing diseases.
Speed
A usual diagnostic approach is scanning a report- be it a CT scan or MRI. AI can scan a report 13 times faster than humans delivering a diagnostic result immediately, helping physicians start a treatment without delay. In the cancer treatment process, the quicker the MRI scan report process, the higher the chances of patients’ survival.
Cost-Effectiveness
In healthcare, AI tools are developed to automate important administrative tasks like scheduling appointments, billing, and claim processing, thereby reducing administrative overhead. The administrative overhead and operational costs are closely related. The reduction of one means the reduction of the other.
Personalized Medicine
When prescribing medicines, physicians follow a general approach that involves assuming the conditions of all patients under a certain category are the same. So they require the same type of drugs. The personalized medicine approach, on the other hand, believes that the condition of each patient is different due to lifestyle, genetic predisposition, and environment. So a certain drug that is appropriate for one patient may not work for the other. AI played a vital role in the emergence of personalized medicine thanks to its algorithm’s ability to analyze vast, and complex data including genetic information, medical records, and lifestyle to correctly diagnose a patient’s diseases, and then prescribe medicines.
Challenges and Limitations
Although you may mistakenly assume that AI is a new technology, it started its journey in the mid-50s. After decades of development, AI is now replacing many traditional health practices. However, several problems have emerged that become real challenges in adopting AI in the health sector.
Data Privacy and Security
AI-enabled healthcare systems are developed based on patients’ sensitive health-related data. This data is privately collected, and stored by different data centers. Here comes the inherent risk of compromisation, and data breach.
Ethical Concerns
Several ethical issues including privacy, data surveillance, biased algorithms, discrimination, and human judgment should be considered when adopting AI in the health sector. While each of these issues is a real concern for patients, unfortunately, no articulated rules or regulations are in place to make patients feel secure embracing AI technology with confidence.
Integration with Existing Systems
Adopting a new technology in healthcare has never been easy. Despite so many expectations that AI will revolutionize the current healthcare system primarily in diagnosing diseases, treatment, and inventing personalized medicines, the adaptation of the former with the later will not be issue less. Challenges that will come along the way to AI are high implementation cost, peoples reluctances and skepticism, and fear of sensible data breach.
AI’s main potential is its ability to analyze large amounts of data and apply machine learning algorithms to identify the correct diseases. So in the future, health professionals will rely more heavily on AI in disease diagnosis. Its use may even extend to analyzing patient’s genomes to determine potential diseases that may affect them.
Different governments set rules and regulations associated with the development and deployment of AI-based diagnostic tools. The general features of these rules are how patients’ sensitive health data would be stored, shared, and privacy would be maintained. Following these rules act as a hurdle for the integration of AI-based diagnostic tools into the existing healthcare system.
The Future of AI in Disease Diagnosis
The field of diagnosis is the heart of healthcare because correct diagnosis is the first step in treating patients correctly. However, the current diagnostic fields are plagued with shortages of expert human resources, excessive workload of lab technicians resulting in human error, unintentional error, and uneven distribution of financial, and resources among different labs. The future of disease diagnosis is bright as the successful integration of AI with this field will bring significant changes in diagnosing diseases. AI diagnostic tools will provide accurate and instant diagnostic results, minimize human error, automate repetitive tasks, reduce treatment time, and save health budget by reducing overhead.
AI will not replace the conventional diagnosis process overnight. However, the healthcare sector will eventually embrace AI-based diagnostic tools in the foreseeable future due to these tools’ exceptional potential to diagnose diseases instantly, and correctly. While the health sector is grappling with human resource shortages- AI will be a good alternative in diagnosing diseases or inventing new medicine.
