The Role of AI in Healthcare: Revolutionizing Diagnosis and Treatment

Artificial intelligence (AI) is transforming the world as we know it, from automating systems to improving the decisions we make and how we make them. But perhaps the most impactful and personal changes AI has caused are within the healthcare sector. The industry has started using AI to create personalized treatment plans and predict patient survival rates.

Nowadays, AI can aid the medical process throughout a medical case, from case data management, diagnosis, blood and body scans, and surgeries to medicine dosage management and rechecking scheduling. Through every step of the way, AI can improve the existing systems and techniques to optimize the whole process for the benefit of the patients, the doctors and the administration. AI has lent a hand in accelerating research for new medicines, new technology, and new pathological discoveries.

How AI Aids Healthcare

AI is an umbrella term covering a variety of distinct but interrelated processes that use computers and machine processes to simulate human intelligence and perform complex automated tasks. Some of the most common forms of AI utilized in the healthcare sector include:

Machine learning (ML)

Machine learning involves training algorithms using data sets, such as health records, images, population data, claims data, and clinical trial data, to create models capable of performing such tasks as categorizing information or predicting outcomes.

Deep learning

It is a subset of machine learning that involves significant volumes of data, training times, and layers of ML algorithms to produce neural networks capable of more complex tasks. Deep Machine Learning reduces the processing time of different laboratory and data management procedures and improves the accuracy of predictions as it involves more variables in the calculations.

Natural language processing (NLP)

NLP employs the use of ML to understand human language, whether it be verbal or written. In healthcare, NLP is used to interpret documentation, notes, reports, and published research by allocating linguistic data compilation and management to AI to reduce such errors at the grassroots level.

Robotic process automation (RPA)

RPA involves using AI in computer programs to automate administrative and clinical workflows. Some healthcare organizations use RPA to improve the patient experience and the daily function of their facilities.

AI presents a wealth of opportunities to healthcare, where it can enhance various standard medical processes — from diagnosing diseases to identifying the best treatment plans for patients facing critical illnesses like cancer. Robotic surgical equipment outfitted with AI can help surgeons better perform surgeries by decreasing their physical fluctuations and providing updated information during the operation.

Multiple organizations throughout the US have started adopting AI in newer forms, opening possibilities on an unprecedented scale for the healthcare sector. Linus Health in Boston provides an AI-based platform to accumulate and analyze 50 metrics of cognitive brain function to determine the early onset of cognitive impairment. Enlitic, a healthcare organization from Colorado, is developing a deep learning tool to optimize radiology feedback and data to provide a more intensive and quicker insight. Freenome is another organization working on developing AI, which can detect patterns of cancer from blood samples from very early on. The Robotics Institute at Carnegie Mellon University developed HeartLander, a miniature mobile robot designed to assist in performing procedures on the heart in the areas which are difficult to access by humans.

Contemporary Challenges in Healthcare And AI

Multiple factors have caused contemporary healthcare to fall short in the quality and accessibility of patient services. Certain flaws, such as rising costs, shortage of workforce, etc., have overtaken the healthcare sector and clipped its wings, hindering its capacity to serve. Using the gifts of AI, these flaws causing these industry-wide issues can be efficiently and permanently dealt with. AI can lead to a perfect set of systems within the healthcare sector by addressing and compensating for the lacks and wants within the systems.

Rising Costs

Healthcare spending is projected to reach $6.2 trillion in 2028 the US alone, driven by factors such as the ageing population, chronic diseases, and expensive treatments. AI can help reduce costs by improving efficiency, accuracy, and quality of care delivery.

Shortage of Workforce

There is a global shortage of health workers estimated at 18 million+ by 2030, especially in low and middle-income countries. AI can help augment human capabilities by automating tasks, providing decision support, and enhancing education and training.

Inequity of Access

Significant disparities in access to quality healthcare across regions, income groups, genders, ethnicities, and other factors exist. AI can help bridge these gaps by enabling remote diagnosis, telemedicine, personalized medicine, and health literacy.

Complexity of Data

Healthcare generates massive amounts of data from various sources such as electronic health records (EHRs), medical imaging, genomics, wearables, social media, etc. AI can help make sense of this data by extracting insights, finding patterns, and generating predictions to improve health outcomes.

Benefits of AI in Healthcare for Patients, Providers, and Payers?

AI has the potential to benefit all stakeholders involved in healthcare delivery by improving quality, efficiency, and satisfaction. The patients receive the most potent benefits of the utility of AI. A more personal and accurate system lends a better probability for the patient to reap the best benefits in the increased effectiveness of therapeutic procedures, from diagnosis and early intervention to scheduled medicine and follow-up session management. Here are a few benefits of AI in healthcare that shine through for the patients as well as the healthcare providers and investors as the new leaps in AI technology shine through the medical field:

Patients

• Improved diagnosis and treatment accuracy and speed
• Enhanced patient engagement and empowerment
• Increased access to care and health information
• Reduced errors and adverse events
• Personalized and preventive care

Providers

• Enhanced clinical decision-making and performance
• Increased productivity and workflow optimization
• Reduced administrative burden and burnout
• Improved collaboration and communication
• Continuous learning and innovation

Payers

• Reduced costs and waste
• Improved quality and value of care
• Increased customer satisfaction and retention
• Enhanced risk management and fraud detection
• Data-driven insights and strategies

Applications of AI in Healthcare Across Different Domains and Specialties

Different domains in the healthcare sector have started availing the benefits of AI advancements. AI’s amalgamation with the medical sciences will keep increasing as more sophisticated advancements occur in AI technology. An AI tool can be trained to fit and fill in the gaps that humans and current resources fail to overcome.

The following domains have started reaping the rewards of AI systems as they aides in accelerated functional efficiency:

Radiology

The results in radiology take their time to be processed. AI can optimize the speed of the process and minimize errors to increase the effectiveness of the therapy assigned based on the radiology results. AI can help analyze medical images such as X-rays, CT scans, MRI scans, ultrasound, etc., to detect abnormalities, measure lesions, segment organs, classify diseases, and monitor progression. For example, IBM Watson Health offers AI solutions for radiology that can help identify critical findings, prioritize cases, and provide clinical insights.

Pathology

AI similarly helps pathology to radiology. More patterns can be found in a vaster landscape as more data is collected to develop a faster, cheaper and more accurate diagnosis. AI can help analyze tissue samples, blood tests, urine tests, etc., to diagnose diseases, identify biomarkers, grade tumours, and predict prognosis. For example, Paige is an AI company that uses deep learning to improve pathology diagnosis and treatment for cancer patients.

Oncology

The research in the oncology domain can accelerate with the help of AI, which increases the possibility of finding a lasting cure for cancer in the coming years. AI can help diagnose cancer, predict survival rates, recommend treatment plans, monitor response, and identify new drug targets. For example, Tempus is an AI company that uses genomic data and machine learning to personalize patient cancer care.

Cardiology

Cardiac conditions are infamous for their silent and intensive risks, which arise due to the unpredictability of such situations. AI can help diagnose cardiac conditions, assess risk factors, predict outcomes, recommend interventions, and monitor heart health. For example, AliveCor is an AI company offering a portable ECG device to detect atrial fibrillation and other heart arrhythmias using a smartphone app.

Neurology

Neurology is one of the domains in the healthcare industry which has lagged for no reason other than the availability of technology that can allow a particular intensity of precision in the results and a lack of a dependable diagnosis system. AI can help diagnose neurological disorders, measure brain activity, stimulate neural pathways, restore function, and enhance cognition. For example, Neuralink is an AI company aiming to develop brain-computer interfaces that enable direct communication between the brain and machines.

Psychiatry

AI can help diagnose mental health conditions, assess mood and emotions, provide therapy, and prevent suicide.

For example, Woebot is an AI company that offers a chatbot that can deliver cognitive behavioural therapy (CBT) to people with depression and anxiety.

These are just some examples of how AI is used in healthcare today. There are many more applications across other domains and specialities, such as dermatology, ophthalmology, endocrinology, gastroenterology, immunology, etc.

Challenges and Limitations of AI in Healthcare and Their Solutions

The advent of AI has acted as a boon for patients and doctors. But some setbacks are expected as the understanding of how AI works is not transparent, and the accountability of AI doesn’t fall under a quantifiable system. This condition poses a threat to an accelerated advancement of the medical field. Therefore, it becomes crucial to monitor, address and resolve the baser issues so that the ramifications do not cut short the benefits of AI’s involvement. Factors such as algorithmic bias, the frequent requirement of monitoring, evaluating and troubleshooting, etc., can hamper the natural progression of AI’s amalgamation with the healthcare sector.

Algorithmic Bias

Algorithmic bias is when an AI makes systematically unfair decisions for specific groups of people. These biases arise due to faulty data management protocols and a lack of pattern recognition precision that can lead to seriously troubling situations. These interpretation-based biases, when manifesting in the form of gender, racial or disability-centred misinterpretations and discrimination, can result in continuing erroneous tendencies. To cure this, the developers writing the AI algorithms have to make sure not to let the inconsistencies and biases of the past data bleed into the AI’s involvement in the process.

Data Quality and Availability

AI models depend on high-quality data to train and validate their performance. However, healthcare data is often fragmented, incomplete, inconsistent, or biased due to various factors such as human errors, privacy regulations, ethical concerns, or lack of standardization. To overcome this challenge, healthcare organizations need to invest in data governance, integration, cleaning, and annotation processes to ensure data quality and availability for AI applications.

Ethical and Legal Issues

AI models may raise ethical and legal issues such as privacy, consent, accountability, transparency, fairness, and safety. For example, how can patients’ data be protected from unauthorized access or misuse? How can patients consent to use their data or apply AI interventions? Who is responsible for the outcomes or errors of AI models? How can AI models explain their decisions or actions to humans? How can AI models avoid discrimination or bias against certain groups or individuals? How can AI models ensure the safety and well-being of patients? To overcome these issues, healthcare organizations must adopt ethical and legal frameworks that can guide developing and deploying AI models in healthcare. These frameworks must include respect for autonomy, beneficence, non-maleficence, justice, and human dignity.

Human-AI Interaction and Trust

AI models may face challenges interacting and collaborating with humans in healthcare settings. For example, how can AI models communicate effectively with patients or providers using natural language or other modalities? How can AI models adapt to different contexts or preferences of users? How can AI models elicit feedback or guide users? How can users trust the accuracy or reliability of AI models? How can users verify or challenge the results or recommendations of AI models? The potential solutions for improving trust between AI and humans include a straightforward education on how AI functions and how to use it best, introducing a more user-oriented design to minimize the assurance gap, etc. If we can find a way to resolve these trust issues, AI can result in helping alleviate the flaws of healthcare to unprecedented lengths.

Conclusion

AI is transforming the healthcare industry in unprecedented ways. From improving diagnosis and treatment to enhancing patient experience and reducing costs, AI is revolutionizing how we care for ourselves and others. We have explored some of the most promising applications of AI in healthcare, such as image analysis, natural language processing, drug discovery, and personalized medicine. We’ve addressed some challenges and ethical issues from AI integration into healthcare. AI is not a magic bullet but a powerful tool that can augment human capabilities and improve health outcomes. As AI continues to evolve and innovate, we expect to see more benefits and opportunities for patients and providers. AI is not only changing healthcare but also changing lives.