Over the past few years, more sectors have been able to benefit from the use of artificial intelligence (AI). Among these sectors, healthcare is experiencing a revolution in how diseases are diagnosed by doctors. From early detection to custom treatment options, AI can fundamentally change the state of health care by improving the level of diagnosis, limiting human faults, and increasing the availability of health care.
In this post, we will look closely at what impact AI is having on medical diagnosis and its component technologies, real life cases, controversies and ethical issues that accompany such development.
Present Day Medical Diagnostic
Traditionally, collection of medical examination data has been a manual workflow influenced by X-rays, MRIs and blood tests. Commonly, medical practitioners have to rely on their experiences, years of training and gut feeling to make a diagnosis and prescribe a treatment plan. It was noted that such system has taken advantage of improvements in medical technology.
How AI Augments/Improves Diagnostic Sensitivity and Accuracy
- Machine Learning and Pattern Recognition:As has been detailed elsewhere, machine learning algorithms are very good at learning complex processes from data and identifying multiple occurrences found in the data.
As an example, AI can recognize such patterns by performing imaging using radiography or CT scanning which may point towards the presence of conditions like cancer or neurological diseases. By training on lots of thousands images, machine learning models can be so good in detecting anomalies that even the human eyes cannot see.
- Natural Language Processing:NLP enables the comprehensible and reusable text with its non-linear structure such as a patients record, research articles, or data from clinical studies. Thus it allows AI to recommend diagnoses and treatment options based on recently acquired knowledge. For example, IBM Watson has analyzed huge volumes of clinical documents and helped the physicians better understand clinical decision making.
- Predictive Analytics:AI can also look at a patient’s previous illness, his or her lifestyle factors, and hereditary history to determine whether the person is at risk for certain diseases. Due to this, it has been possible to prevent most diseases especially diabetes, heart diseases, and other cancers from progressing too far for normal treatment.
Applying artificial intelligence in the assessment of illnesses, in the present world
The augmentation of AI within medical diagnosis is a not just a pipe dream, there are a lot of real world use cases that are changing the game.
- Oncology
One of the most notable areas, it seems to this author also, for conducting diagnostics using AI is oncology. Other than that, AI technologies are being employed to process medical images at earlier stages of development than conventional methods and they claim to detect cancer. DeepMind is another example where artificial intelligence systems developed by their scientists outperform human radiologists in diagnosing breast cancer. In the same manner, PathAI is attempting to increase quality of cancer diagnosis through innovative pathology imaging further by developing novel platforms for disease-specific AI-based imaging analysis.
- Cardiology
Moving on to cardiology, earning enhancement through effectual use of AI is also progressed. Eko’s Duo is a smart stethoscope that can detect atrial fibrillation and heart murmurs out of heart sounds using machine learning algorithms. Furthermore, heart failure prediction has been accomplished through the ECG results of the patients, thanks to the AI developed algorithms from Mayo Clinic.
- Neurology
AI is used in neurology to assist in the diagnosis and monitoring of Alzheimer’s disease, Parkinson’s disease and multiple sclerosis. To illustrate, the ingenious MENTIONED AI DISPOSABLE DEVICES manufactured by Medtronic incorporate self-learned capabilities for detecting the presence of neurodegenerative disease at its early stages for timely management.
- Ophthalmology: Verily, owned by Google, has developed one of the algorithms based on deep learning capable of diagnosing diabetic retinopathy. Regular screening is a lengthy process requiring specialized training, however, the algorithm developed by Verily is able to quickly evaluate retinal photographs, streamlining the screening process.
5. Dermatology: Skin cancer is an area where AI is actually proving its worth. Mobile AI systems can evaluate the condition of skin and even determine the likelihood of a melanoma developing. There are also applications like SkinVision that permit to track the skin over time, providing a thorough kangaroo review assessing the risk under the guidance of a dermatologist.
Challenges and Ethical Considerations for AI in Medical Imaging
Although there are considerable advantages for the application of AI in diagnostics, there is a need to highlight the challenges and ethical issues involved in this technology.
- Data Privacy and Security:
As mentioned above, the performance AI has in diagnosing is predominantly dependent on the availability of large amounts of medical records. Protecting the confidentiality and security of this information is of utmost importance. There are some restrictions legislated like in Europe – the General Data Protection Regulation (GDPR) and in America the Health Insurance Portability and Accountability Act (HIPAA) as to how such data is collected, stored and utilized. Even so, being able to comply with these requirements and protect the patients’ data continues to be a big issue.
- Bias and Fairness:Algorithms used in AI are often able to learn the societal biases present in the available data and further worsen them. For example, an AI trained from data provided majorly by one group is likely to yield poor results when used on other groups and thus, results in gaps in diagnosis. To minimize such bias, it is necessary to make concerted efforts in employing such efforts to clear those biases.
- Regulatory Hurdles:The introduction of AI tools in commonplace clinical practice faces a few hurdles none other than regulatory issues. This is particularly true for organizations like FDA, who are still gazing into the distance trying to find the way to approve AI based medical devices. Regulation is important in that it guarantees the safety and effectiveness of these measures, however, it has been noted to limit the rate of development.
- Clinical Integration:If AI diagnosis has to be relevant in practice, it should therefore be placed within already existing clinical environments. This encompasses the use of processes such as EHR systems, the use of telemedicine, as well as the standard diagnostic procedures. The ability of the new tools to interface with the available ones, as well as ease of acceptability will influence how deeply AI will penetrate into everyday medical practice.
- Ethical Implications of AI Use in Diagnostics:
Infrastructural deficits render the efficient use of AI in Diagnostic Imaging still ethically shaky, as lack of accountability and transparency still remain. For instance in a situation where an AI system is used to diagnose a patient and the diagnosis turns to be wrong, Who should be blamed? All stakeholders must appreciate the need for clear communication of the workings of AI algorithms in delivery of services so as not to erode the already limited confidence among the healthcare practitioners and their patients.
The Future Trends and Applications of AI in Medical Diagnostics
In spite of the problems, the prospects of AI in medical diagnosis are extraordinarily bright indeed. Keeping this in mind, here are some trends and advancements to watch for:
- Personalized Medicine:
AI is capable of changing the face of personalized medicine by ensuring that the mode of treatment is specific for that one particular patient. Genetic Information, lifestyle and some other cofactors are coupled with the use of AI in order to produce upgraded treatment strategies with minimal complications.
- Global Healthcare Access:
It is possible to fill the gap where there are deficient resources in health care services from areas that are deprived as well as those that are far away. Telemedicine AI driven platforms would be able to give you accurate quality health advice with no doctors being present physically which is hugely beneficial in areas where doctors are limited.
- Continuous Learning and Improvement:One of the key positive facts related to AI is that it can learn and enhance itself continuously. With more and more updates and new data supplied, AI algorithms are bound to improve in their accuracy over time and hence make it more useful or accurate in disease diagnosis.
- Integration with Wearable Technology:Devices such as smartwatches and fitness bands are turning out to be more advanced as they provide features that track key vital signs and essential health parameters all the time. Applying AI on those devices would allow real-time diagnostics and notifications to the users of the devices well before it reaches a critical stage.
- Global Healthcare Access:AI may correct inaccessibility of healthcare with special focus in regions that are remote and under-resourced. Healthcare services can also be rendered through the use of AI stakeholders who make telemedicine possible. This is helpful in areas where there is a shortage of medical personnel.
- Continuous Learning and Improvement:AI has the most special characteristic: the ability to improve with time, and learn new skills. The AI approaches have been developing over the years with updates and better data which have enabled these systems to get better in detection of diseases.
- Integration with Wearable Technology:Such smart health technologies as smartwatches and fitness bands are becoming more complex, extending their functionality to 24/7 health tracking. The use of AI on these types of devices is beneficial in that it makes it possible to do life-saving diagnosis even before the probable cause of the disease becomes critical.
- AI-Driven Drug Discovery:Other than changing diagnosis, AI is also changing the drug discovery process. AI has become capable of finding drug candidates faster accurately by understanding biochemical interactions and patient’s genetic data.
Conclusion
The introduction of AI-based systems to assist healthcare workers in diagnosing patients constitutes a major landmark in the history of the field due to its superiority and perfor mance in time and cost. Although the field of AI assisted strategy is still relatively young and the level of integration is still tender, it offers a lot of opportunities for the future. In all the ethical and practical challenges posed by this technology, one persuasion remains foremost: improve surgical results, and improve the whole treatment process as well.
The most impressive impact of AI-based systems on healthcare is that they have migrated the emphasis from the opportunities in technology to the way we percei and deliver health care. It is with this concept where the future lies, one where every individual has a unique health care that caters for them without discrimination.
Professionals within healthcare, law, and politics and ordinary interested individuals should pay attention to these cuts as they constitute the future. The future of AI for practical use in assessing the medical needs of patients is not far out derived sense; it is happening today as far as the architecture of healthcare dispensation is concerned.
References
Given the academic nature of this topic, it’s essential to cite reputable sources:
1. Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
2. Gulshan, V., et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402-2410.
3. Rajpurkar, P., et al. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint arXiv:1711.05225.
4. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
5. Zhao, Q., et al. (2020). Deep Learning Models for Predicting Severe COVID-19 Outcomes Incorporating Chest X-ray Images, Clinical Data and Reinforcement Learning Methods. arXiv preprint arXiv:2011.12897.