Evolution and Applications of Artificial Intelligence to Cataract Surgery

Artificial intelligence in ophthalmology

PURPOSE OF REVIEW

To review role of artificial intelligence in medicine.

RECENT FINDINGS

Artificial intelligence is continuing to revolutionize access, diagnosis, personalization of medicine, and treatment in healthcare. As a matter of fact, artificial intelligence contributed to the research that resulted in 2024 Nobel Prizes in physics, chemistry, and economics. We are only at the tip of the iceberg in utilizing the abilities of artificial intelligence in medicine to improve accuracy of diagnoses and to enhance patient outcomes. Artificial intelligence has allowed better image analysis, prediction of progression of disease, personalized treatment plans, incorporations of genomics, and improved efficiency in care and follow-up utilizing home monitoring. In ocular health diagnosis and treatment of diabetic retinopathy, macular degeneration, glaucoma, corneal infections, and ectasia are only a few examples of how the power of artificial intelligence has been harnessed. Even though there are still challenges that need more work in the areas of patient privacy, Health Insurance Portability and Accountability Act (HIPAA) compliance, reliability, and development of regulatory frameworks, artificial intelligence has revolutionized and will continue to revolutionize medicine.

SUMMARY

Artificial intelligence is enhancing medical diagnosis and treatment, as well as access and prevention. Ocular imaging, visual outcome, optics, intraocular pressure, and data points will continue to see growth it the field of artificial intelligence.

Applications of Artificial Intelligence in Cataract Surgery: A Review

Cataract surgery is one of the most performed procedures worldwide, and cataracts are rising in prevalence in our aging population. With the increasing utilization of artificial intelligence (AI) in the medical field, we aimed to understand the extent of present AI applications in ophthalmic microsurgery, specifically cataract surgery. We conducted a literature search on PubMed and Google Scholar using keywords related to the application of AI in cataract surgery and included relevant articles published since 2010 in our review. The literature search yielded information on AI mechanisms such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) as they are being incorporated into pre-operative, intraoperative, and post-operative stages of cataract surgery. AI is currently integrated in the pre-operative stage of cataract surgery to calculate intraocular lens (IOL) power and diagnose cataracts with slit-lamp microscopy and retinal imaging. During the intraoperative stage, AI has been applied to risk calculation, tracking surgical workflow, multimodal imaging data analysis, and instrument location via the use of "smart instruments". AI is also involved in predicting post-operative complications, such as posterior capsular opacification and intraocular lens dislocation, and organizing follow-up patient care. Challenges such as limited imaging dataset availability, unstandardized deep learning analysis metrics, and lack of generalizability to novel datasets currently present obstacles to the enhanced application of AI in cataract surgery. Upon addressing these barriers in upcoming research, AI stands to improve cataract screening accessibility, junior physician training, and identification of surgical complications through future applications of AI in cataract surgery.

Evaluation of Large language model performance on the Multi-Specialty Recruitment Assessment (MSRA) exam

INTRODUCTION

AI-powered platforms have gained prominence in medical education and training, offering diverse applications from surgical performance assessment to exam preparation. This research paper examines the capabilities of Large Language Models (LLMs), including Llama 2, Google Bard, Bing Chat, and ChatGPT-3.5, in answering multiple-choice questions of the Clinical Problem Solving (CPS) paper of the Multi-Specialty Recruitment Assessment (MSRA) exam.

METHODS

Using a dataset of 100 CPS questions from ten subject categories, we assessed the LLMs' performance against medical doctors preparing for the exam.

RESULTS

Results showed that Bing Chat outperformed all other LLMs and even surpassed human users from the Qbank question bank. Conversely, Llama 2's performance was inferior to human users. Google Bard and ChatGPT 3.5 did not exhibit statistically significant differences in correct response rates compared to human candidates. Pairwise comparisons demonstrated Bing Chat's significant superiority over Llama 2, Google Bard, and ChatGPT 3.5. However, no significant differences were found between Llama 2 and Google Bard, Llama 2, and ChatGPT-3.5, and Google Bard and ChatGPT-3.5.

DISCUSSION

Freely available LLMs have already demonstrated that they can perform as well or even outperform human users in answering MSRA exam questions. Bing Chat emerged as a particularly strong performer. The study also highlights the potential for enhancing LLMs' medical knowledge acquisition through tailored fine-tuning. Medical knowledge tailored LLMs such as Med-PaLM, have already shown promising results.

CONCLUSION

We provided valuable insights into LLMs' competence in answering medical MCQs and their potential integration into medical education and assessment processes.

An update on intraocular lens power calculations in eyes with previous laser refractive surgery

PURPOSE OF REVIEW

There is an ever-growing body of research regarding intraocular lens (IOL) power calculations following photorefractive keratectomy (PRK), laser-assisted in-situ keratomileusis (LASIK), and small-incision lenticule extraction (SMILE). This review intends to summarize recent data and offer updated recommendations.

RECENT FINDINGS

Postmyopic LASIK/PRK eyes have the best refractive outcomes when multiple methods are averaged, or when Barrett True-K is used. Posthyperopic LASIK/PRK eyes also seem to do best when Barrett True-K is used, but with more variable results. With both aforementioned methods, using measured total corneal power incrementally improves results. For post-SMILE eyes, the first nontheoretical data favors raytracing.

SUMMARY

Refractive outcomes after cataract surgery in eyes with prior laser refractive surgery are less accurate and more variable compared to virgin eyes. Surgeons may simplify their approach to IOL power calculations in postmyopic and posthyperopic LASIK/PRK by using Barrett True-K, and employing measured total corneal power when available. For post-SMILE eyes, ray tracing seems to work well, but lack of accessibility may hamper its adoption.

Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations

Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as "ophthalmic surgery", "surgical selection", "candidate screening", and "robot-assisted surgery" to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.