Thanks to the development of truly useful and powerful generative artificial intelligence, the role of experts in society is changing. All industries will be affected, but we already know that the data- and technology-intensive healthcare industry will be more disrupted than other industries.
Generative AI has the potential to revolutionize the way we treat diseases, develop new drugs, and personalize treatments for individual patients. It will also fundamentally change the daily working lives of doctors, nurses, and other clinical health professionals, and even how they are viewed by society. As a result, they will find themselves relying more than ever on human qualities such as compassion, communication, and the instincts that many people in these jobs have for providing care.
Here, therefore, is my overview of some of the most dramatic and meaningful changes expected in the near future, and some of the practical and ethical challenges that will need to be overcome.
AI as a diagnostic assistant
Generative AI helps diagnose conditions by interpreting data and providing clear, detailed insights into what is known about the patient. With it, you can examine hundreds of her X-rays, MRIs, and CT scans and quickly get a statistical summary of the results. This enables more accurate, data-driven diagnosis of many common and less common conditions.
This communication can be fine-tuned depending on the role of the healthcare professional using it, such as a doctor, nurse, consultant, or specialist. Communicating only the insights that are relevant to you means there is less noise between experts and the specific information they need.
The World Economic Forum also predicts that generative AI will enable data to be efficiently extracted from the many disparate and siled sources that previously existed across healthcare, leading to improved outcomes. Masu.
It is also increasingly used to create synthetic data. This is especially useful in situations where training data is limited, such as rare conditions or diseases. It also reduces the security and data protection workload that healthcare professionals have to bear when handling personal data provided by real patients. Synthetic data can also be used to simulate medical scenarios such as pandemics or the emergence of antibiotic-resistant bacteria that could cause a global medical crisis.
Automate routine and administrative tasks
It will become increasingly common for healthcare professionals to use generated AI to automate many of the repetitive and mundane administrative tasks they perform every day. This frees up their time to focus on providing direct care, as well as continuing their training and learning.
From managing and updating patient records to scheduling appointments, healthcare professionals engage in many time-consuming tasks that AI could streamline or even take over completely. there is. According to one study, physicians spend half of their workday performing tasks related to maintaining electronic health records (EHRs).
Generative AI can drive more efficient EHR management by intelligently organizing physician notes, test results, and medical images. You can then provide a brief overview of individual patients, highlight health aspects of concern, and create reports for other professionals. Automating many of these tasks may also have the effect of reducing errors that can impact quality of care and patient outcomes.
Generative AI in drug discovery
The same capabilities that generative AI can use to create text and sentences can also be used to develop new drug candidates and vaccines for clinical trials. This means researchers can shorten the long process of narrowing down candidates to a shortlist.
Last year, Oxford-based biotech company Etcembly produced the first immunotherapy drug created with the help of generative AI.
This process is expected to accelerate the transition of potentially life-saving new treatments from the laboratory to patients, ultimately improving patient outcomes. This means that medical researchers and scientists, just like doctors and nurses, will have powerful generative AI tools that will allow them to work faster and more efficiently.
Ethical considerations – the human touch
However, it is clear that integrating generative AI into healthcare in this way creates a long list of ethical challenges that cannot be ignored. This is because most use cases revolve around the use of personal data. This means protection against data leaks, loss, and breaches is of paramount importance.
It is also essential that AI algorithms make transparent and explainable decisions. This is critical to building the public trust needed to realize the system’s potential.
The damage that data bias can cause is also more pronounced than in almost any other field. It has been shown that generative AI models can amplify biases present in the training data. We know that women and people from ethnic minority backgrounds are underrepresented in medical research and are diagnosed more often, and this problem is likely to grow as AI becomes more widely used. there is.
To reduce these biases, data, models, and results all need to be continuously monitored and updated. Failure to do so could further perpetuate inequality.
Healthcare professionals, like many other professionals, will find themselves needing to learn the skillset of an AI ethicist. This will develop the ability to evaluate potential use cases to determine whether the application of AI could cause harm, risk, or danger, and ensure that appropriate guardrails are always in place. means to guarantee.
What does the future hold for doctors and healthcare workers?
Doctors, nurses, and other clinical health professionals are probably more protected from the risk of being replaced by AI than many others. Their jobs require them to function at a high level across many human skills that machines cannot readily imitate. Intuition and experience all play an important role, and that will always be the case.
But AI offers these professionals the opportunity to redefine the way they work and even their role in wider society. Moving to a work model that allows you to spend more time with patients means more time to continue your continuing education and develop your own medical expertise.
This will increase the need for clinical staff focused on AI-enhanced diagnostics, data-driven medicine, and ethical AI to help patients navigate the range of new AI-assisted treatment options that will become available. This is likely to lead to new areas of specialization. .
With AI handling routine analysis, recordkeeping and interpretation of scans, imaging data, and other data, doctors and nurses have more time to get to the bottom of more complex and nuanced patient problems. will be spent.
Ultimately, the essence of healthcare delivery will continue to revolve around empathy, compassion, and humanity. Generative AI creates opportunities to enhance these qualities in ways that make experts in this field even more essential to society. Those who can embrace this paradigm shift will find that they can use their skills and training to treat disease and improve the lives of patients in ever more rewarding ways.
follow me twitter Or LinkedIn. check out My website and other works can be found here.
