While the possibilities artificial intelligence delivers to the healthcare industry are immense, there are legitimate concerns about the way hospitals, clinics and pharmaceutical companies that develop new drugs and therapies will operate in the future. Does artificial intelligence have the potential to revolutionize the way healthcare works? What obstacles stand in the way of the wider application of AI capabilities in the medical industry? Read on to find out.
AI in healthcare
Artificial intelligence in the biotech and healthcare industry is nothing new. As numerous studies have shown, the global market for artificial intelligence (AI) for healthcare is growing rapidly, and this pace will continue in the coming years as well. The current value of the market is estimated at $14.6 billion but is expected to reach $102.7 billion as early as 2028, which translates into a CAGR of an impressive 47.6%. The numbers could be even higher, but the shortage of skilled AI professionals and the lack of clear regulations for medical software are bound to adversely affect this momentum.
Another significant challenge to continued market growth comes from a high degree of patient distrust towards artificial intelligence. This is confirmed by the results of a survey conducted by the Pew Research Center, which indicate that as many as 60% of Americans would not be comfortable with a healthcare provider that relies on AI. Only 38% of those surveyed agreed with the statement that artificial intelligence used for diagnosing diseases and recommending treatment would lead to better outcomes. As many as 33% of respondents were convinced that the effects would be worse, while the rest of the respondents said it would not make much difference.
Why use AI for healthcare?
There is no denying that healthcare, in many places around the world, does not function efficiently. It is underfunded and managed in a very chaotic way. This translates not only into a shortage of professional medical equipment or personal protective equipment, but also immense staff shortages, which result in poor quality treatment and long wait times to see a specialist.
Many of the problems currently facing health services, as well as the broader medical industry, can be effectively addressed by artificial intelligence. It is already successfully doing so today. Examples of the use of AI in healthcare are numerous, and all signs indicate that this trend will only increase in the coming years. Why? The reason is simple – artificial intelligence can do work in a fraction of the time it would take a human to do. It can also work hand in hand with staff to improve the quality of services provided in hospitals or clinics, reduce the risk of oversights and improve the way medical facilities operate.
For example, machine learning models have been trained, from hundreds of X-rays or CT scans, to learn how to identify worrisome lesions effectively. They have taken diagnostics to a whole new level and reduced the risk of overlooking newly developing conditions to a minimum. In a very similar way, AI supports scientists who are responsible for developing new drugs or therapies. Conducting more tests and verifying hypotheses consumes a lot of time. Properly trained models can handle this task in a very short time and thus significantly speed up the entire process.
The need for greater use of AI in the area of research and testing by pharmaceutical companies, for example, was visible during the COVID-19 pandemic. The race to create an effective vaccine involved many pharmaceutical companies, but it was won by those who made the right use of artificial intelligence capabilities. A prime example is AstraZeneca, which was one of the first pharmaceutical companies to use AI, and very successfully so. By using artificial intelligence at every stage of the R&D process, the company is now able to produce new drugs and therapies not only faster, but also more affordably and safely.
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Benefits of AI in healthcare
Every year, AI models are becoming more sophisticated and more effective at mimicking the way the human brain behaves. This means that the possibilities of applying artificial intelligence are constantly growing – enabling healthcare providers to address increasingly complex and demanding processes and make a bigger difference in the way hospitals and clinics and treat patients.
The most important reason why the healthcare industry is turning to AI is to increase the quality of services provided – faster identification of worrisome lesions, more effective diagnoses, the ability to remotely monitor a patient’s health in real-time, or to automate decision-making processes and many administrative tasks in hospitals and clinics. Above all, the pharmaceutical industry appreciates AI’s tremendous support at literally every stage of the development process for new drugs and therapies. This makes it possible to address the needs reported by patients more quickly and effectively, which generate tangible benefits for an organization.
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Use cases – How can AI solve healthcare problems?
Many problems facing the healthcare industry today can be effectively addressed with a properly designed and trained algorithm. Therefore, it should come as no surprise that AI is already widely used in many areas, including:
- Enhanced diagnosis and treatment – analyzing medical records, laboratory results and imaging scans to make effective and timely diagnoses and create personalized treatment plans.
- Improved efficiency and productivity – freeing employees from routine administrative tasks (making appointments, managing medical records, invoicing, etc.).
- Precision medicine – genomic and molecular analysis to design personalized treatment plans that take into account individual traits and genetic predispositions or biomarkers.
- Remote patient monitoring – continuous collection and monitoring of patient data (vital signs, sleep patterns, etc.), making it possible to track the progress of treatment and also to take timely preventive action.
- Drug discovery and development – analysis of massive biomedical data sets to accelerate production and improve the efficacy of new drugs and therapies.
- Patient engagement and education – chatbots and virtual assistants that provide personalized medical information and guidance on chronic disease self-management.
- Resource allocation and optimization – efficient allocation of resources (inventory, hospital beds, staff) to increase efficiency, reduce costs and improve service quality and patient satisfaction.
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Challenges for AI in healthcare
The benefits of implementing artificial intelligence solutions in the medical industry are undeniable. It is thus natural at this point to raise a question regarding the reasons for the industry’s still relatively low use of AI technology. One of the primary reasons is finance. The implementation of advanced machine learning models requires the prior creation of an appropriate system infrastructure. It is also necessary to involve qualified specialists in the process, which entails considerable costs. The health service has been complaining for years about a shortage of equipment and basic personal protective equipment, so it’s hard to demand that an already cash-strapped budget set aside funds for supercomputers capable of processing huge volumes of data in fractions of a second.
Pharmaceutical companies are in a slightly different position. They have considerable budgets and are increasingly willing to allocate them to AI solutions. This is born out by the results of a survey conducted in December 2021 by US-based healthcare provider Optum – the “4th Annual Optum Survey on AI in Health Care.” As many as 85% of healthcare executives say they have an AI strategy, and half are already using the technology.
Other than finance, what other challenges does the healthcare industry face as regards AI implementations? The biggest hurdles include:
- Data quality and availability – the smooth operation of machine learning models requires access to large volumes of high-quality data. Their availability in the medical industry is often severely limited – the data is fragmented, often incomplete and, on top of that, stored in many different formats, which severely hinders its effective use.
- Privacy and security – medical data is sensitive and subject to stringent privacy regulations. The designed systems for collection, storage and subsequent analysis must ensure a high level of privacy and security.
- Bias and fairness – machine learning algorithms may inadvertently perpetuate biases present in the data they are trained on, resulting in differences in treatment or diagnosis. It is therefore crucial to eliminate them and ensure equal treatment of patients.
- Interpretability and transparency – decisions made by an algorithm in the medical industry can determine the health and even the lives of patients. It is extremely important that it operates as transparently as possible. Without this, it may prove impossible to gain the trust of medical professionals and patients.
- Regulatory and ethical considerations – the use of AI capabilities in medical care raises questions about the responsibilities and roles of healthcare professionals. It is necessary to implement clear rules and guidelines to ensure responsible and ethical use of machine learning models.
- Validation and verification of AI models – before implementing models in real clinical settings, it is essential to properly test them. Robust validation processes, especially in the medical industry, play a huge role. They must indicate that the designed system is safe, effective and that it provides reliable results on which the decision-making process may safely be based.
- Integration with existing systems – one of the most prominent challenges associated with implementing any AI solution in the healthcare industry is integrating it with an existing system infrastructure. Implementing a solution that requires a lot of work and which is not used effectively by employees misses the point.
The plethora of challenges and problems in implementing AI in the healthcare field, on the one hand, discourages investment, while on the other, pushes technology giants to develop new solutions which can then be adopted by the industry. Prime examples of such initiatives include the Tokyo-1 supercomputer, created by Mitsui and NVIDIA to accelerate the discovery of new drugs and therapies by Japan’s leading pharmaceutical companies, or Google’s large-scale language model Med-PaLM, the equivalent of Chat GPT-3, trained on reliable medical data. Certainly, there will soon be many more similar initiatives, as well as scalable and easy-to-implement solutions.
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About the authorSoftware Mind
Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world.